IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Human factors in software reliability engineering - Research PaperMuhammad Ahmad Zia
In this paper, Maria Spichkova et al. have presented theri vision of the integration of human factors engineering into the software development process. The aim of this approach is to improve the quality of software and to deal with human errors in a systematic way.
Categories and Subject Descriptors
D.4.5 [Reliability]: Fault-tolerance; D.2.5 [Software Engineering]: Testing and Debugging
General Terms
Reliability, Verification
This document discusses software metrics and measurement. It defines key terms like measure, metric, indicator, and defines different types of metrics like process, project, and product metrics. It explains that metrics are needed for effective management and decision making. Metrics allow managers to assess quality, productivity, and benefits over time. The document also discusses guidelines for using metrics and normalizing metrics to allow comparison across projects.
The document discusses software test management and planning. It notes that errors found early in the development process are less costly to fix. A graph shows that errors discovered during maintenance are 368 times more expensive to fix than requirements errors. The document recommends optimizing the software process to find errors early. It also provides guidance on test planning, including designing for testability, defining metrics, covering all requirements with tests, and integrating the test plan into the project plan.
Software Engineering : Software Quality assurance : Software product metrics and their categories for measuring the support service parameters offered through Software Service Helpdesk
This document discusses different types of software metrics including process, product, and project metrics. It defines metrics as quantitative measures of attributes and discusses how they can be used as indicators to improve processes and projects. Process metrics measure attributes of the development process over long periods of time. Product metrics measure attributes of the software at different stages. Project metrics are used to monitor and control projects. The document also discusses size-oriented and function-oriented metrics for normalization and comparison purposes. It provides examples of calculating function points and deriving metrics like errors per function point.
This document discusses metrics that can be used to measure software processes and projects. It begins by defining software metrics and explaining that they provide quantitative measures that offer insight for improving processes and projects. It then distinguishes between metrics for the software process domain and project domain. Process metrics are collected across multiple projects for strategic decisions, while project metrics enable tactical project management. The document outlines various metric types, including size-based metrics using lines of code or function points, quality metrics, and metrics for defect removal efficiency. It emphasizes integrating metrics into the software process through establishing a baseline, collecting data, and providing feedback to facilitate continuous process improvement.
This document discusses software metrics that can be used to measure process and project attributes. It defines key terms like measurement, measure, metric and indicator. It describes different types of metrics like process metrics, project metrics, size-oriented metrics, function-oriented metrics and quality metrics. It also discusses concepts like defect removal efficiency and redefining defect removal efficiency to measure effectiveness of quality assurance activities.
Human factors in software reliability engineering - Research PaperMuhammad Ahmad Zia
In this paper, Maria Spichkova et al. have presented theri vision of the integration of human factors engineering into the software development process. The aim of this approach is to improve the quality of software and to deal with human errors in a systematic way.
Categories and Subject Descriptors
D.4.5 [Reliability]: Fault-tolerance; D.2.5 [Software Engineering]: Testing and Debugging
General Terms
Reliability, Verification
This document discusses software metrics and measurement. It defines key terms like measure, metric, indicator, and defines different types of metrics like process, project, and product metrics. It explains that metrics are needed for effective management and decision making. Metrics allow managers to assess quality, productivity, and benefits over time. The document also discusses guidelines for using metrics and normalizing metrics to allow comparison across projects.
The document discusses software test management and planning. It notes that errors found early in the development process are less costly to fix. A graph shows that errors discovered during maintenance are 368 times more expensive to fix than requirements errors. The document recommends optimizing the software process to find errors early. It also provides guidance on test planning, including designing for testability, defining metrics, covering all requirements with tests, and integrating the test plan into the project plan.
Software Engineering : Software Quality assurance : Software product metrics and their categories for measuring the support service parameters offered through Software Service Helpdesk
This document discusses different types of software metrics including process, product, and project metrics. It defines metrics as quantitative measures of attributes and discusses how they can be used as indicators to improve processes and projects. Process metrics measure attributes of the development process over long periods of time. Product metrics measure attributes of the software at different stages. Project metrics are used to monitor and control projects. The document also discusses size-oriented and function-oriented metrics for normalization and comparison purposes. It provides examples of calculating function points and deriving metrics like errors per function point.
This document discusses metrics that can be used to measure software processes and projects. It begins by defining software metrics and explaining that they provide quantitative measures that offer insight for improving processes and projects. It then distinguishes between metrics for the software process domain and project domain. Process metrics are collected across multiple projects for strategic decisions, while project metrics enable tactical project management. The document outlines various metric types, including size-based metrics using lines of code or function points, quality metrics, and metrics for defect removal efficiency. It emphasizes integrating metrics into the software process through establishing a baseline, collecting data, and providing feedback to facilitate continuous process improvement.
This document discusses software metrics that can be used to measure process and project attributes. It defines key terms like measurement, measure, metric and indicator. It describes different types of metrics like process metrics, project metrics, size-oriented metrics, function-oriented metrics and quality metrics. It also discusses concepts like defect removal efficiency and redefining defect removal efficiency to measure effectiveness of quality assurance activities.
Software metricsIntroduction
Attributes of Software Metrics
Activities of a Measurement Process
Types
Normalization of Metrics
Help software engineers to gain insight into the design and construction of the software
Activities of a Measurement Process
To answer this we need to know the size & complexity of the projects.
But if we normalize the measures, it is possible to compare the two
For normalization we have 2 ways-
Size-Oriented Metrics
Function Oriented Metrics
Software Engineering Practice - Software Metrics and EstimationRadu_Negulescu
This document discusses software metrics and estimation. It introduces common product and project metrics like lines of code, function points, time and effort. It describes the basis for estimation, including measures of size/scope and baseline data from previous projects. It notes the uncertainty in estimation and discusses techniques like lines of code, function points and estimation rules of thumb. The goal is to provide metrics to answer key questions for planning, monitoring and controlling a software project.
This document discusses different types of software metrics that can be used to measure and evaluate software projects and processes. It defines key terms like measure, measurement, and metric. It explains that metrics are used to indicate quality, assess productivity, evaluate new methods/tools, and form baselines for estimation. The main types of metrics discussed are process metrics, which measure the development process, and project metrics, which are used to monitor and control software projects. Examples of different metrics include lines of code, defects, cost, effort, size-oriented metrics, and function-oriented metrics. The document provides details on calculating and applying function points as a type of function-oriented metric.
Software metrics can be used to measure various attributes of software products and processes. There are direct metrics that immediately measure attributes like lines of code and defects, and indirect metrics that measure less tangible aspects like functionality and reliability. Metrics are classified as product metrics, which measure attributes of the software product, and process metrics, which measure the software development process. Project metrics are used tactically within a project to track status, risks, and quality, while process metrics are used strategically for long-term process improvement. Common software quality attributes that can be measured include correctness, maintainability, integrity, and usability.
Software metrics are quantitative measures used to characterize aspects of software, like size, quality, and complexity. They are used for estimating costs and schedules, controlling projects, predicting quality, providing management information, and process improvement. There are three main categories of metrics: product metrics measure attributes of the software itself like size and reliability; process metrics assess the effectiveness of development processes; and project metrics help managers track project status, risks, and quality. Key roles of metrics include monitoring requirements, predicting resource needs, tracking processes, understanding maintenance costs, and improving software through measurement.
This document discusses software metrics and how they can be used to measure various attributes of software products and processes. It begins by asking questions that software metrics can help answer, such as how to measure software size, development costs, bugs, and reliability. It then provides definitions of key terms like measurement, metrics, and defines software metrics as the application of measurement techniques to software development and products. The document outlines areas where software metrics are commonly used, like cost estimation and quality/reliability prediction. It also discusses challenges in implementing metrics and provides categories of metrics like product, process, and project metrics. The remainder of the document provides examples and formulas for specific software metrics.
This document discusses software metrics. It defines a software metric as a standard measure of a software system or process's properties. The document then classifies metrics into product, process, and resource metrics. It describes several types of product metrics including size, complexity, Halstead's, and quality metrics. The document recommends using a GQM (Goal, Question, Metric) approach to implement an effective metrics program. It provides references for further reading.
This document discusses software metrics and measurement. It describes how measurement can be used throughout the software development process to assist with estimation, quality control, productivity assessment, and project control. It defines key terms like measures, metrics, and indicators and explains how they provide insight into the software process and product. The document also discusses using metrics to evaluate and improve the software process as well as track project status, risks, and quality. Finally, it covers different types of metrics like size-oriented, function-oriented, and quality metrics.
This document discusses software quality metrics and classifies them into two categories: process metrics and product metrics. Process metrics are related to the software development process and include software process quality metrics, timetable metrics, error removal effectiveness metrics, and productivity metrics. Product metrics are related to software maintenance and customer service. The document provides examples of specific metrics like error density metrics, error severity metrics, and high-definition quality and productivity metrics.
The document discusses different types of software metrics that can be used to measure various aspects of software development. Process metrics measure attributes of the development process, while product metrics measure attributes of the software product. Project metrics are used to monitor and control software projects. Metrics need to be normalized to allow for comparison between different projects or teams. This can be done using size-oriented metrics that relate measures to the size of the software, or function-oriented metrics that relate measures to the functionality delivered.
This chapter discusses software estimation, measurement, and metrics. It explains that accurate size estimation is critical for determining cost, schedule, and effort but is often too low, leading to budget overruns and delays. It describes various size estimation techniques including source lines of code, function points, and feature points. It also discusses complexity metrics and the importance of requirements management. The chapter emphasizes that software measurement provides visibility into program status and facilitates early problem detection. An effective measurement program should be integrated throughout the lifecycle and the data used to manage the program. If problems are found, measurement enables taking corrective actions.
This document discusses various software metrics that can be used for software estimation, quality assurance, and maintenance. It describes black box metrics like function points and COCOMO, which focus on program functionality without examining internal structure. It also covers white box metrics, including lines of code, Halstead's software science, and McCabe's cyclomatic complexity, which measure internal program properties. Finally, it discusses using metrics like change rates and effort adjustment factors to estimate software maintenance costs.
The document discusses software engineering metrics and quality assurance. It covers:
- Why measurement is important in software engineering for objective evaluation, estimation, quality control, and improvement.
- Types of software metrics including direct metrics like lines of code and indirect metrics like functionality.
- Frameworks for measuring software quality attributes like correctness, maintainability, integrity, and usability.
- The importance of software quality assurance in reducing costs and improving time-to-market through defining quality, identifying assurance activities, and using metrics for process improvement.
The document discusses various software project size estimation metrics. It describes the limitations of lines of code (LOC) counting, such as variability due to coding style and not accounting for non-coding effort. Function point analysis and feature point analysis are presented as alternatives that overcome some LOC limitations by basing size on software features rather than lines. The key steps of function point analysis involve counting types of inputs, outputs, inquiries and other parameters to calculate unadjusted function points which are then adjusted based on technical complexity factors. While more accurate than LOC, function point analysis is still subjective based on how parameters are defined and counted.
Software Engineering (Metrics for Process and Projects)ShudipPal
The document discusses software process measurement and metrics. Some key points:
1. Measurement is fundamental to software engineering as it allows processes to be evaluated and improved continuously. Metrics can be used for estimation, quality control, productivity assessment, and project control.
2. Process metrics are collected across projects over long periods to provide indicators for long-term process improvements. Project metrics enable managers to assess status, track risks, and adjust tasks.
3. Guidelines for metrics include using common sense, providing feedback, not evaluating individuals, setting clear goals, and not threatening teams. Metrics should indicate problem areas for improvement, not be considered negative.
The document discusses validation processes for critical systems, including reliability validation, safety assurance, security assessment, and developing safety and dependability cases. Validation of critical systems involves additional processes compared to non-critical systems, such as formal validation to demonstrate that requirements are met to regulators' satisfaction. Reliability validation assesses whether a system meets required reliability levels through representative testing, while safety assurance establishes confidence in a system's safety through processes, validation techniques, and reviews. Security assessment and safety assurance both aim to demonstrate that systems cannot enter unsafe or insecure states. Safety and dependability cases make detailed arguments through evidence that systems achieve required safety levels.
Bca 5th sem seminar(software measurements)MuskanSony
This document discusses software measurement and different types of metrics. It covers size-oriented metrics like lines of code, function-oriented metrics like function points that measure functionality, and extended function point metrics. Software measurement provides quantitative attributes of software products and processes to assess quality and assist with project management decisions. Measures can be direct, measured from the project itself, or indirect, where attributes are not immediately quantifiable.
The document discusses various metrics that can be collected during software testing. It describes metrics for the requirements phase like requirement stability index and requirements leakage index. For the test design phase, it discusses metrics like test case preparation productivity. Metrics for the test execution phase include test case pass percentage and test case execution percentage. Defect metrics covered are defect summary, defect discovery rate, defect severity, defect density, and defect rejection ratio. Formulas for calculating these metrics are provided along with descriptions of what each metric measures.
Software Development Metrics You Can Count On Parasoft
When trying to analyze software quality today, we have a bewildering array of possible metrics. Some purport to be the one true answer. Which metrics mean what? Which ones can you trust? Which ones can be dangerous? Learn how to get the most out of your software metrics.
This ppt covers the following topics
Software quality
A framework for product metrics
A product metrics taxonomy
Metrics for the analysis model
Metrics for the design model
Metrics for maintenance
Este documento presenta 70 casos de innovación apoyados por INNOVA Chile de CORFO entre 2000 y 2006. INNOVA Chile busca impulsar la innovación en empresas chilenas para aumentar la competitividad del país. Los casos muestran diferentes tipos de innovación como nuevos productos, procesos mejorados, materiales y modelos de negocios. Algunas innovaciones son resultado de I+D mientras otras adoptan tecnologías externas. Juntos, estos casos ilustran cómo la innovación se ha vuelto clave para las estrategias empresariales en Chile.
This document provides information about various topics related to Lean leadership and Lean project delivery. It discusses making Lean easy to understand for organizations, the importance of flow and value in Lean. It also summarizes a talk by Paul O'Neill as CEO of Alcoa about prioritizing worker safety, which increased the company's market value. Additional sections promote networking with Lean leaders, and an online course on Lean project delivery practices to create high performance green projects.
Software metricsIntroduction
Attributes of Software Metrics
Activities of a Measurement Process
Types
Normalization of Metrics
Help software engineers to gain insight into the design and construction of the software
Activities of a Measurement Process
To answer this we need to know the size & complexity of the projects.
But if we normalize the measures, it is possible to compare the two
For normalization we have 2 ways-
Size-Oriented Metrics
Function Oriented Metrics
Software Engineering Practice - Software Metrics and EstimationRadu_Negulescu
This document discusses software metrics and estimation. It introduces common product and project metrics like lines of code, function points, time and effort. It describes the basis for estimation, including measures of size/scope and baseline data from previous projects. It notes the uncertainty in estimation and discusses techniques like lines of code, function points and estimation rules of thumb. The goal is to provide metrics to answer key questions for planning, monitoring and controlling a software project.
This document discusses different types of software metrics that can be used to measure and evaluate software projects and processes. It defines key terms like measure, measurement, and metric. It explains that metrics are used to indicate quality, assess productivity, evaluate new methods/tools, and form baselines for estimation. The main types of metrics discussed are process metrics, which measure the development process, and project metrics, which are used to monitor and control software projects. Examples of different metrics include lines of code, defects, cost, effort, size-oriented metrics, and function-oriented metrics. The document provides details on calculating and applying function points as a type of function-oriented metric.
Software metrics can be used to measure various attributes of software products and processes. There are direct metrics that immediately measure attributes like lines of code and defects, and indirect metrics that measure less tangible aspects like functionality and reliability. Metrics are classified as product metrics, which measure attributes of the software product, and process metrics, which measure the software development process. Project metrics are used tactically within a project to track status, risks, and quality, while process metrics are used strategically for long-term process improvement. Common software quality attributes that can be measured include correctness, maintainability, integrity, and usability.
Software metrics are quantitative measures used to characterize aspects of software, like size, quality, and complexity. They are used for estimating costs and schedules, controlling projects, predicting quality, providing management information, and process improvement. There are three main categories of metrics: product metrics measure attributes of the software itself like size and reliability; process metrics assess the effectiveness of development processes; and project metrics help managers track project status, risks, and quality. Key roles of metrics include monitoring requirements, predicting resource needs, tracking processes, understanding maintenance costs, and improving software through measurement.
This document discusses software metrics and how they can be used to measure various attributes of software products and processes. It begins by asking questions that software metrics can help answer, such as how to measure software size, development costs, bugs, and reliability. It then provides definitions of key terms like measurement, metrics, and defines software metrics as the application of measurement techniques to software development and products. The document outlines areas where software metrics are commonly used, like cost estimation and quality/reliability prediction. It also discusses challenges in implementing metrics and provides categories of metrics like product, process, and project metrics. The remainder of the document provides examples and formulas for specific software metrics.
This document discusses software metrics. It defines a software metric as a standard measure of a software system or process's properties. The document then classifies metrics into product, process, and resource metrics. It describes several types of product metrics including size, complexity, Halstead's, and quality metrics. The document recommends using a GQM (Goal, Question, Metric) approach to implement an effective metrics program. It provides references for further reading.
This document discusses software metrics and measurement. It describes how measurement can be used throughout the software development process to assist with estimation, quality control, productivity assessment, and project control. It defines key terms like measures, metrics, and indicators and explains how they provide insight into the software process and product. The document also discusses using metrics to evaluate and improve the software process as well as track project status, risks, and quality. Finally, it covers different types of metrics like size-oriented, function-oriented, and quality metrics.
This document discusses software quality metrics and classifies them into two categories: process metrics and product metrics. Process metrics are related to the software development process and include software process quality metrics, timetable metrics, error removal effectiveness metrics, and productivity metrics. Product metrics are related to software maintenance and customer service. The document provides examples of specific metrics like error density metrics, error severity metrics, and high-definition quality and productivity metrics.
The document discusses different types of software metrics that can be used to measure various aspects of software development. Process metrics measure attributes of the development process, while product metrics measure attributes of the software product. Project metrics are used to monitor and control software projects. Metrics need to be normalized to allow for comparison between different projects or teams. This can be done using size-oriented metrics that relate measures to the size of the software, or function-oriented metrics that relate measures to the functionality delivered.
This chapter discusses software estimation, measurement, and metrics. It explains that accurate size estimation is critical for determining cost, schedule, and effort but is often too low, leading to budget overruns and delays. It describes various size estimation techniques including source lines of code, function points, and feature points. It also discusses complexity metrics and the importance of requirements management. The chapter emphasizes that software measurement provides visibility into program status and facilitates early problem detection. An effective measurement program should be integrated throughout the lifecycle and the data used to manage the program. If problems are found, measurement enables taking corrective actions.
This document discusses various software metrics that can be used for software estimation, quality assurance, and maintenance. It describes black box metrics like function points and COCOMO, which focus on program functionality without examining internal structure. It also covers white box metrics, including lines of code, Halstead's software science, and McCabe's cyclomatic complexity, which measure internal program properties. Finally, it discusses using metrics like change rates and effort adjustment factors to estimate software maintenance costs.
The document discusses software engineering metrics and quality assurance. It covers:
- Why measurement is important in software engineering for objective evaluation, estimation, quality control, and improvement.
- Types of software metrics including direct metrics like lines of code and indirect metrics like functionality.
- Frameworks for measuring software quality attributes like correctness, maintainability, integrity, and usability.
- The importance of software quality assurance in reducing costs and improving time-to-market through defining quality, identifying assurance activities, and using metrics for process improvement.
The document discusses various software project size estimation metrics. It describes the limitations of lines of code (LOC) counting, such as variability due to coding style and not accounting for non-coding effort. Function point analysis and feature point analysis are presented as alternatives that overcome some LOC limitations by basing size on software features rather than lines. The key steps of function point analysis involve counting types of inputs, outputs, inquiries and other parameters to calculate unadjusted function points which are then adjusted based on technical complexity factors. While more accurate than LOC, function point analysis is still subjective based on how parameters are defined and counted.
Software Engineering (Metrics for Process and Projects)ShudipPal
The document discusses software process measurement and metrics. Some key points:
1. Measurement is fundamental to software engineering as it allows processes to be evaluated and improved continuously. Metrics can be used for estimation, quality control, productivity assessment, and project control.
2. Process metrics are collected across projects over long periods to provide indicators for long-term process improvements. Project metrics enable managers to assess status, track risks, and adjust tasks.
3. Guidelines for metrics include using common sense, providing feedback, not evaluating individuals, setting clear goals, and not threatening teams. Metrics should indicate problem areas for improvement, not be considered negative.
The document discusses validation processes for critical systems, including reliability validation, safety assurance, security assessment, and developing safety and dependability cases. Validation of critical systems involves additional processes compared to non-critical systems, such as formal validation to demonstrate that requirements are met to regulators' satisfaction. Reliability validation assesses whether a system meets required reliability levels through representative testing, while safety assurance establishes confidence in a system's safety through processes, validation techniques, and reviews. Security assessment and safety assurance both aim to demonstrate that systems cannot enter unsafe or insecure states. Safety and dependability cases make detailed arguments through evidence that systems achieve required safety levels.
Bca 5th sem seminar(software measurements)MuskanSony
This document discusses software measurement and different types of metrics. It covers size-oriented metrics like lines of code, function-oriented metrics like function points that measure functionality, and extended function point metrics. Software measurement provides quantitative attributes of software products and processes to assess quality and assist with project management decisions. Measures can be direct, measured from the project itself, or indirect, where attributes are not immediately quantifiable.
The document discusses various metrics that can be collected during software testing. It describes metrics for the requirements phase like requirement stability index and requirements leakage index. For the test design phase, it discusses metrics like test case preparation productivity. Metrics for the test execution phase include test case pass percentage and test case execution percentage. Defect metrics covered are defect summary, defect discovery rate, defect severity, defect density, and defect rejection ratio. Formulas for calculating these metrics are provided along with descriptions of what each metric measures.
Software Development Metrics You Can Count On Parasoft
When trying to analyze software quality today, we have a bewildering array of possible metrics. Some purport to be the one true answer. Which metrics mean what? Which ones can you trust? Which ones can be dangerous? Learn how to get the most out of your software metrics.
This ppt covers the following topics
Software quality
A framework for product metrics
A product metrics taxonomy
Metrics for the analysis model
Metrics for the design model
Metrics for maintenance
Este documento presenta 70 casos de innovación apoyados por INNOVA Chile de CORFO entre 2000 y 2006. INNOVA Chile busca impulsar la innovación en empresas chilenas para aumentar la competitividad del país. Los casos muestran diferentes tipos de innovación como nuevos productos, procesos mejorados, materiales y modelos de negocios. Algunas innovaciones son resultado de I+D mientras otras adoptan tecnologías externas. Juntos, estos casos ilustran cómo la innovación se ha vuelto clave para las estrategias empresariales en Chile.
This document provides information about various topics related to Lean leadership and Lean project delivery. It discusses making Lean easy to understand for organizations, the importance of flow and value in Lean. It also summarizes a talk by Paul O'Neill as CEO of Alcoa about prioritizing worker safety, which increased the company's market value. Additional sections promote networking with Lean leaders, and an online course on Lean project delivery practices to create high performance green projects.
The Knowledge Management Role In Mitigating Operational RiskEduardo Longo
The document discusses the relationship between knowledge management and operational risk mitigation. It uses the 1986 Challenger space shuttle disaster as an example of how better knowledge management could have prevented the catastrophe. Specifically, it describes how NASA engineers had concluded cold temperatures posed a risk to the O-rings but failed to effectively present the evidence. The document proposes integrating knowledge management and operational risk approaches to identify how information and knowledge can create operational risk events and how they could be used to avoid such events.
The document discusses error management and rectification in accounting. It outlines three steps to error management: prevention, detection, and rectification of errors. It describes different types of errors and methods for detecting errors, including trial balances. The key aspects of rectification are identifying the wrong and correct entries, reversing the wrong entry, and determining the rectification entry. The stages of rectification depend on whether errors are identified before or after accounts are finalized.
This document provides guidance on error management using tools like RCA, 5Whys, and CAPAs. It outlines how to define the problem, understand the root cause using 5Whys, and plan and implement corrective and preventive actions. The 5Whys method involves asking "Why?" repeatedly to determine the root cause of an error. It recommends documenting the error, taking immediate containment actions, investigating to find the likely cause, and using 5Whys to identify the root and contributing causes in order to implement effective corrective and preventive actions. Using 5Whys helps separate symptoms from underlying causes and get to the systemic root of problems.
The Five Things To Remember Before You ChangeAndrew Valenti
Whether you are currently unemployed, underemployed, or seeking a change, these are the essential points to consider before your make your next job move.
Development of software defect prediction system using artificial neural networkIJAAS Team
Software testing is an activity to enable a system is bug free during execution process. The software bug prediction is one of the most encouraging exercises of the testing phase of the software improvement life cycle. In any case, in this paper, a framework was created to anticipate the modules that deformity inclined in order to be utilized to all the more likely organize software quality affirmation exertion. Genetic Algorithm was used to extract relevant features from the acquired datasets to eliminate the possibility of overfitting and the relevant features were classified to defective or otherwise modules using the Artificial Neural Network. The system was executed in MATLAB (R2018a) Runtime environment utilizing a statistical toolkit and the performance of the system was assessed dependent on the accuracy, precision, recall, and the f-score to check the effectiveness of the system. In the finish of the led explores, the outcome indicated that ECLIPSE JDT CORE, ECLIPSE PDE UI, EQUINOX FRAMEWORK and LUCENE has the accuracy, precision, recall and the f-score of 86.93, 53.49, 79.31 and 63.89% respectively, 83.28, 31.91, 45.45 and 37.50% respectively, 83.43, 57.69, 45.45 and 50.84% respectively and 91.30, 33.33, 50.00 and 40.00% respectively. This paper presents an improved software predictive system for the software defect detections.
Towards formulating dynamic model for predicting defects in system testing us...Journal Papers
This document discusses developing a dynamic model for predicting defects in system testing using metrics collected from prior phases. It begins with background on the waterfall and V-model software development processes. It then reviews previous research on software defect prediction, noting limited work has focused specifically on predicting defects in system testing. The proposed model would analyze metrics collected during requirements, design, coding, and testing phases to determine which metrics best predict defects found in system testing. A case study is discussed that would apply statistical analysis to historical metrics data to formulate a mathematical equation for defect prediction. The model would then be verified by applying it to new projects and comparing predicted defects to actual defects found during system testing. The goal is to select a prediction model that estimates defects
Parameter Estimation of GOEL-OKUMOTO Model by Comparing ACO with MLE MethodIRJET Journal
The document presents a comparison of the Ant Colony Optimization (ACO) method and Maximum Likelihood Estimation (MLE) method for parameter estimation of the Goel-Okumoto software reliability growth model. It describes using the ACO and MLE methods to estimate unknown parameters of the Goel-Okumoto model based on ungrouped time domain failure data. The key parameters estimated are a, which represents the expected total number of failures, and b, which represents the failure detection rate. The document aims to determine which of these two parameter estimation methods can best identify failures at early stages of software reliability monitoring.
This document discusses software reliability and fault discovery probability analysis. It begins by defining software reliability as consisting of error prevention, fault discovery and removal, and reliability measurements. A beta distribution model is proposed to analyze the probability of discovering faults during software testing. The document evaluates different parameter estimation methods for the beta distribution model like variance, sum of squares, and maximum likelihood estimation. It analyzes the performance of these parameter estimation methods using sample programs. The document concludes that estimating failure rates from different faults under different testing measures can provide a prior evaluation of a model's parameters and predict testing effort required to achieve quality goals.
Using Fuzzy Clustering and Software Metrics to Predict Faults in large Indust...IOSR Journals
This document describes a study that uses fuzzy clustering and software metrics to predict faults in large industrial software systems. The study uses fuzzy c-means clustering to group software components into faulty and fault-free clusters based on various software metrics. The study applies this method to the open-source JEdit software project, calculating metrics for 274 classes and identifying faults using repository data. The results show 88.49% accuracy in predicting faulty classes, demonstrating that fuzzy clustering can be an effective technique for fault prediction in large software systems.
The document discusses using data mining techniques to analyze crime data and predict crime trends. It describes collecting crime reports from various sources to create a database. Machine learning algorithms would then be applied to the crime data to discover patterns and relationships between different crimes. This analysis could help police identify crime hotspots and determine if a crime was committed in a known location. The proposed system aims to forecast crimes and trends based on past crime data, date and location to help prevent crimes. It discusses implementing the system using Python and testing it with sample input data.
Information hiding based on optimization technique for Encrypted ImagesIRJET Journal
This document summarizes a research paper on reversible data hiding in encrypted images using an optimization technique. The paper proposes an algorithm that first identifies the area of interest in an encrypted image and then uses a Bat Algorithm to find noisy pixel coordinates for embedding text data. Any remaining data is embedded in the image border areas. The research aims to securely protect embedded data against attacks while maintaining efficiency. It discusses related work on separable reversible data hiding techniques and the need for reversible data hiding in encrypted images to maintain confidentiality while allowing lossless image recovery.
A Combined Approach of Software Metrics and Software Fault Analysis to Estima...IOSR Journals
The document presents a software fault prediction model that uses reliability relevant software metrics and a fuzzy inference system. It proposes predicting fault density at each phase of development using relevant metrics for that phase. Requirements metrics like complexity, stability and reviews are used to predict fault density after requirements. Design, coding and testing metrics are similarly used to predict fault densities after their respective phases. The model aims to enable early identification of quality issues and optimal resource allocation to improve reliability. MATLAB is used to define fault parameters, categories, fuzzy rules and analyze results. The goal is a multistage fault prediction model for more reliable software delivery.
A survey of predicting software reliability using machine learning methodsIAESIJAI
In light of technical and technological progress, software has become an urgent need in every aspect of human life, including the medicine sector and industrial control. Therefore, it is imperative that the software always works flawlessly. The information technology sector has witnessed a rapid expansion in recent years, as software companies can no longer rely only on cost advantages to stay competitive in the market, but programmers must provide reliable and high-quality software, and in order to estimate and predict software reliability using machine learning and deep learning, it was introduced A brief overview of the important scientific contributions to the subject of software reliability, and the researchers' findings of highly efficient methods and techniques for predicting software reliability.
This document discusses defect prediction models in software development. It begins by covering the importance of effort estimation in software maintenance planning and management. The document then discusses how data from software defect reports, including details on defects, components, testers and fixes, can be used to build reliability models to predict remaining defects. Machine learning and data mining techniques are proposed to analyze relationships between software quality across releases and to construct predictive models for forecasting time to fix defects. The document provides an overview of typical software development processes and then discusses a two-step approach to defect prediction and analysis using appropriate statistics and data mining techniques.
Developing software analyzers tool using software reliability growth modelIAEME Publication
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Developing software analyzers tool using software reliability growth modelIAEME Publication
The document discusses developing a software analyzer tool using a software reliability growth model to improve software quality. It proposes an Enhanced Non-Homogeneous Poisson Process (ENHPP) model to estimate software reliability measures like remaining faults and failure rate. The ENHPP model explicitly incorporates a time-varying testing coverage function and allows for imperfect debugging and coverage changes over testing and operation. It is validated on real failure data sets and shown to provide better fit than existing models. The goal is to enhance code reusability, minimize test effort estimation and improve reliability through the testing phase of the software development life cycle.
A Complexity Based Regression Test Selection StrategyCSEIJJournal
Software is unequivocally the foremost and indispensable entity in this technologically driven world.
Therefore quality assurance, and in particular, software testing is a crucial step in the software
development cycle. This paper presents an effective test selection strategy that uses a Spectrum of
Complexity Metrics (SCM). Our aim in this paper is to increase the efficiency of the testing process by
significantly reducing the number of test cases without having a significant drop in test effectiveness. The
strategy makes use of a comprehensive taxonomy of complexity metrics based on the product level (class,
method, statement) and its characteristics.We use a series of experiments based on three applications with
a significant number of mutants to demonstrate the effectiveness of our selection strategy.For further
evaluation, we compareour approach to boundary value analysis. The results show the capability of our
approach to detect mutants as well as the seeded errors.
This document summarizes a research paper that examines the use of data mining techniques to predict software aging-related bugs from imbalanced datasets. The paper compares the performance of general data mining techniques versus techniques developed for imbalanced datasets on a real-world dataset of aging bugs found in MySQL software. The results show that techniques designed for imbalanced datasets, such as SMOTEbagging and MSMOTEboosting, performed better than general techniques at correctly predicting the minority class of data points related to aging bugs. The paper concludes that imbalanced dataset techniques are more useful for predicting rare aging bugs from imbalanced software bug datasets.
FROM THE ART OF SOFTWARE TESTING TO TEST-AS-A-SERVICE IN CLOUD COMPUTINGijseajournal
Researchers consider that the first edition of the book "The Art of Software Testing" by Myers (1979)
initiated research in Software Testing. Since then, software testing has gone through evolutions that have
driven standards and tools. This evolution has accompanied the complexity and variety of software
deployment platforms. The migration to the cloud allowed benefits such as scalability, agility, and better
return on investment. Cloud computing requires more significant involvement in software testing to ensure
that services work as expected. In addition to testing cloud applications, cloud computing has paved the
way for testing in the Test-as-a-Service model. This review aims to understand software testing in the
context of cloud computing. Based on the knowledge explained here, we sought to linearize the evolution of
software testing, characterizing fundamental points and allowing us to compose a synthesis of the body of
knowledge in software testing, expanded by the cloud computing paradigm.
From the Art of Software Testing to Test-as-a-Service in Cloud Computingijseajournal
Researchers consider that the first edition of the book "The Art of Software Testing" by Myers (1979)
initiated research in Software Testing. Since then, software testing has gone through evolutions that have
driven standards and tools. This evolution has accompanied the complexity and variety of software
deployment platforms. The migration to the cloud allowed benefits such as scalability, agility, and better
return on investment. Cloud computing requires more significant involvement in software testing to ensure
that services work as expected. In addition to testing cloud applications, cloud computing has paved the
way for testing in the Test-as-a-Service model. This review aims to understand software testing in the
context of cloud computing. Based on the knowledge explained here, we sought to linearize the evolution of
software testing, characterizing fundamental points and allowing us to compose a synthesis of the body of
knowledge in software testing, expanded by the cloud computing paradigm.
IRJET- A Novel Approach on Computation Intelligence Technique for Softwar...IRJET Journal
This document describes a study that uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict software defects early in the development process. The study uses metrics data from NASA software projects to train and test the ANFIS model. The results show that the ANFIS model is able to accurately predict defects, with low root mean square error values for both the training and testing data, indicating the model was able to generalize without overfitting. The study concludes ANFIS is an effective technique for software defect prediction that can help improve quality and reduce costs.
The document describes an automated process for bug triage that uses text classification and data reduction techniques. It proposes using Naive Bayes classifiers to predict the appropriate developers to assign bugs to by applying stopword removal, stemming, keyword selection, and instance selection on bug reports. This reduces the data size and improves quality. It predicts developers based on their history and profiles while tracking bug status. The goal is to more efficiently handle software bugs compared to traditional manual triage processes.
Test Case Optimization and Redundancy Reduction Using GA and Neural Networks IJECEIAES
More than 50% of software development effort is spent in testing phase in a typical software development project. Test case design as well as execution consume a lot of time. Hence, automated generation of test cases is highly required. Here a novel testing methodology is being presented to test objectoriented software based on UML state chart diagrams. In this approach, function minimization technique is being applied and generate test cases automatically from UML state chart diagrams. Software testing forms an integral part of the software development life cycle. Since the objective of testing is to ensure the conformity of an application to its specification, a test “oracle” is needed to determine whether a given test case exposes a fault or not. An automated oracle to support the activities of human testers can reduce the actual cost of the testing process and the related maintenance costs. In this paper, a new concept is being presented using an UML state chart diagram and tables for the test case generation, artificial neural network as an optimization tool for reducing the redundancy in the test case generated using the genetic algorithm. A neural network is trained by the backpropagation algorithm on a set of test cases applied to the original version of the system.
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A Review on Software Fault Detection and Prevention Mechanism in Software Development Activities
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 6, Ver. V (Nov – Dec. 2015), PP 25-30
www.iosrjournals.org
DOI: 10.9790/0661-17652530 www.iosrjournals.org 25 | Page
A Review on Software Fault Detection and Prevention
Mechanism in Software Development Activities
B.Dhanalaxmi1
, Dr.G.Apparao Naidu2
, Dr.K.Anuradha3
1
Associate Professor, Institute of Aeronautical Engineering, IT Dept,Hyderabad,TS,India
2
Professor , J.B.Institute of Engineering & Technology, CSE Dept, Hyderabad,TS,India
3
Professor & HOD, GRIET, CSE Dept, Hyderabad,TS,India
Abstract: The need of distributed and complex commercial applications in enterprise demands error free and
quality application systems. This makes it extremely important in software development to develop quality and
fault free software. It is also very important to design reliable and easy to maintain as it involves a lot of human
efforts, cost and time during software life cycle. A software development process performs various activities to
minimize the faults such as fault prediction, detection, prevention and correction. This paper presents a survey
on current practices for software fault detection and prevention mechanisms in the software development. It
also discusses the advantages and limitations of these mechanisms which relates to the quality product
development and maintenance.
Keywords: Software development, Fault Detection, Fault Prevention, Software faults
I. Introduction
The software is a single entity which has established a strong impact on all the domain software which
includes education, defence, medical, scientific, transportation, telecommunications and others. The activities of
this domain always demands for high quality software for their accurate service need [1], [2], [3]. Software
quality means to be an error-free product, which will be competent to produce predictable results and able to
deliver within the constraints of time and cost. Therefore, a systematic approach for developing high quality
software is increased in the competitiveness in today's business world, technology advances, the complexity of
the hardware and the changing business requirements. So far, for the fault-prone modules various techniques
have been proposed for predicting and forecasting in terms of performance evaluation. However, the kind of
quality improvement and cost reduction as their actual need to meet the business objectives is rarely assessed.
Software failures are mainly caused by design deficiencies that occur when a software engineer, either
misunderstood a specification or simply makes an error. It is estimated that 60-90% of current computer errors
are caused due to the software failures [10],[12], [19]. These failure predictions has been studied in the context
of fault-prone modules, self healing systems, developer information, maintenance models, etc., but a lot of
things like modelling and weighting of the impact of different types of faults in different types of software
systems must be explored for the fault severity in software development.
Performance requirements and reliability are fundamental to the development of high assurance
systems. Based on the failure analysis it has proved a useful tool for detecting and preventing failures
requirements early in the software lifecycle. Adapting a generic taxonomy fault, one is able to better prevent
past mistakes and develop requirements specifications with less general failures. Fewer failures in the software
specification, with respect to the requirements for performance and reliability, will result in high security and
quality systems. The scope of this paper is to provide an overview of the mechanism in fault detection and
techniques for the prevention of faults that can be followed in the quality software development process.
The following paper organizes in the seven sections. Section-2 and 3 discuss about software fault
detection and software fault preventions mechanism. Section-4 presents fault prevention benefits and its
limitation, section-5 presents related works and section-6 presents the conclusion.
II. Software Fault Detection Mechanism
A failure refers to any fault or imperfection in a work activity for a software product or software
process cause due to an error, fault or failure. The IEEE Standards defines the terms Error as, a human action
that leads to inaccurate results, Fault as, a wrong decision while understanding the information given to solve
the problems or the application process. A single error can lead to one or more faults and a several faults can
lead to failure. To avoid this failure in software products, faults detections activities are carried out in every
phase of the software development life cycle based on their need and criticality.
A Monden et al. [1] proposes simulation model using fault prediction results for software testing to measure the
cost effectiveness of test effort allocation strategies. The proposed model evaluates the number of qualified
2. A Review on Software Fault Detection and Prevention Mechanism in Software Development Activities
DOI: 10.9790/0661-17652530 www.iosrjournals.org 26 | Page
faults in relates to resource allocation strategy, a set of modules, and the result of fault prediction. In a case
study applying a small fault prediction system acceptance testing in the telecommunications industry, the results
of our simulation model showed that the best strategy was to let the test effort is proportional to "number of
failures expected in a module ". By using this strategy with our best prediction model of failure, the test effort
reduced by 25%, while detecting as flawed normally found in testing, even if the company requires
approximately 6% of the test effort for the collection of statistics, data cleansing and modelling.
A. Detection Using Automated Static Analysis
Automated Static Analysis (ASA) detection is mostly performed for the Manual Code analysis, which
is one of the oldest practices are still practiced, but automated tools are increasingly used especially for the
standard problems related to non-compliance faults possible memory leaks, variable usage etc. They have an
essential place in the development phase because they save effort and significant resumption fault leakage test
cycles. Findbugs, CheckStyle and PMD are some of the commonly used tools in the Java technology and there
are many of these tools in all technologies. Although this plays an important role in the development cycle is not
widely practiced in the maintenance mode. However, for systems that have compatible source for automatic
static analysis detection tools can be used as a hygiene factor and good detection mechanism as any error
introduced in the field is highly expensive. Maintenance cycle of ASA detection tools cannot find many flaws
that may result in failures. A study on the effectiveness of ASA detection tools in the open source code reveals
that less than 3% of the failures [2].
S Liu et al.[3] address static analysis technique problem that is commonly used for fault detection, but
which suffers from a lack of rigor. It supports a systematic and rigorous inspection method takes advantage of
the formal specification and analysis. The purpose of the method defined in the specification of a set of paths
from each functional landscape program and the path specification of the program in every program contributes
to the implementation of a functional landscape that is implemented correctly determine whether the inspection
is used. Specification of functional scenarios to get the program paths, the paths linking scenarios, analyzing the
paths against the scenarios, and the production of an inspection report, and a list of a systematic and automatic
generation for inspection.
B. Detection Using Graph mining
Graph Mining is a dynamic control flow based approach that helps identify flaws that may be not
crashing in nature. Use graphics calls are reduced by the simplicity in processing. The graph node represents the
functions and a function call to another is represented by the edges. Edge weights are entered based on the
calling frequencies. The variation in the frequency of call and change in the structure of call are potential
failures. If there are problems in the data that is transmitted between the methods could also affect the graph of
the named because of its implications.
C. Detection Using Classifiers
Classifiers based on the clustering algorithm and decision tree or neural network can be used to identify
abnormal events of normal events for the detections. Classifiers are also formed by labelling defective tracks
when a fault is observed. Some classifiers are commonly used NaiveBayes and bagging. Bayesian classification
is a supervised learning method and a statistical method for classification. Representing an underlying
probabilistic model that allows us to capture the uncertainty in the model of a reasoned determining the
probabilities of outcomes. Recent research works [4] done in this area, without secondary supervision model
that captures the normal code of behaviour probability distribution of each region is proposed to identify events
when it behaves abnormally. This information is used to filter the labelling abnormality submitted to the ranking
algorithm to focus on anomalous observations.
Machine learning classifiers [35] have recently introduced in the faults to predict changes in the source
files. The classifier is first trained on software development, and then used to predict whether an upcoming
change causes an error. Disadvantages of existing classifier-based bug prediction techniques are not enough
power for practical use and slow prediction times due to a large number of machines learned functions.
S Shivaji et al. [5] investigates several feature selection techniques, which are generally for
classification based fault prediction method using Naive Bayes and Support Vector Machine (SVM) classifiers.
The techniques discard less important functions until optimal classification performance achieved. The total
number of functions used for the formation is substantially reduced, often to less than 10 percent of the original.
Both Naive Bayes and SVM with feature selection provides significant improvement in Buggy F-measure
compared to the prior classification change failure prediction results compare to proposed in [6], work.
3. A Review on Software Fault Detection and Prevention Mechanism in Software Development Activities
DOI: 10.9790/0661-17652530 www.iosrjournals.org 27 | Page
D. Detection Using Pattern Mining
Pattern based detection also the classifier based but uses unique iterative patterns for classification
sequential data using the software trace analysis for failure detection. A set of discriminatory features capture
repetitive series of events from the program execution traces first executed. Subsequently, the choice is made to
select the best features for classification. Classifier model is trained with these sets of features that will be used
to identify the failures. Processing pattern modelling allows together the analysis and improvement of processes,
the work coordinate multiple people and tools to perform a task. Process modelling focuses generally on the
normative process that is how transpires cooperation, if all goes as desired. Unfortunately, real-world processes
rarely go that smoothly. A more complete analysis of the process requires that the process model and details of
what to do when emergency situations occur.
B.S. Lerner et al. [7] have shown that in many cases there are abstract pattern to detect the relationship
between the exception handling functions and the normative process. Just as object-oriented design patterns
facilitate the development, documentation and maintenance of object-oriented programs, they believe that
process patterns can facilitate the development, documentation and maintenance of process models. They focus
on the exception handling pattern that we have observed over many years of process modelling. They also
describe these patterns using three process modelling notations: UML 2.0 Activity Diagram [8], BPMN and
Little-JIL [9]. They provide both the abstract structure of the pattern, as well as an example of the pattern is
used. They present some preliminary statistical data to support the contention that these patterns are commonly
found in practice, and represent in relation to their ability to use these patterns to discuss the relative merits of
the three notations.
III. Software Fault Prevention Mechanism
In software development, many faults emerged during the development process. It is a mistake to
believe that faults are injected into the beginning of the cycle and removed through the rest of the development
process [10]. The faults occur all the way through the development process. Therefore, fault prevention becomes
an essential part of improving the quality of software processes.
Fault prevention is a process of quality improvement which aims to identify common causes of faults
and change the relevant processes to prevent the type of fault recurrence. It also increases the quality of a
software product and reduces overall costs, time and resources. This ensures that a project can keep the time,
cost and quality in balance. The purpose of fault prevention is to identify faults in the beginning of the life cycle
and prevent it happening again so that the fault cannot appear again.
A. Importance of Fault Prevention
Faults prevention is an important activity in any software project development cycle. Most software project
team focuses on fault detection and correction. Thus, fault prevention, often becomes a neglected component.
Right from the early stages of the project to prevent faults from being introduced into the product that measure
is therefore appropriate to make. Such measures are low cost, the total cost savings achieved due to profit later
on stage are quite high compared to the cost of fixing faults. Thus, the time required for the analysis of faults in
the early stages, reducing the cost and resources. Fault injection methods and processes enable fault prevention
knowledge. After practicing this knowledge has improved quality. It also enhances overall productivity.
B. Activities in Fault Prevention
Fault Identification
Fault can be a pre-planned activities aimed at highlighting the specific faults found. In general, faults
can be identified in design review, code inspection, GUI Review, function and unit testing activities performed
at different stages of software development life cycle. Once the faults are identified it will be classified using
classification approach for the detection.
Fault Classification
Classification of fault can be made using the general Orthogonal Defect Classification (ODC)
technique [11] to find the fault group and it type. The ODC technique classifies the faults at the time when fault
first occurs and when the fault gets fixed. The ODC methodology for each fault in orthogonal (mutually
exclusive) to certain technology and some managerial Characteristics. These characteristics change through
massive amounts of data can be analyzed and the root cause, the pattern to be able to access all the information
on offer. Good action planning and tracking across with this fault reduction and can achieve high levels of
learning.
Generally, important projects which are typically large projects needs to be classified in depth in order
to get analyze and understand the faults, while the small and medium projects can be classified faults up to first
level of ODC in order to save time and effort. The first level of ODC classifies the various types of faults in
different stages of development requirement like Specification gathering, Logical Design, Testing and
Documentation.
4. A Review on Software Fault Detection and Prevention Mechanism in Software Development Activities
DOI: 10.9790/0661-17652530 www.iosrjournals.org 28 | Page
Fault Analysis
Fault analysis is the continuous process for the quality improvement using fault data. Fault analysis
generally classified in categories blame and direct process improvement efforts in order to attempt to identify
possible causes. Root Cause Analysis (RCA) software fault has played a useful role in the analysis. RCA's goal
to identify the root cause of faults and flaws that the source is eliminated so is to initiate action. To do this,
faults one at a time are analyzed. Qualitative analysis is limited only by the limits of human investigative
capacities. Qualitative analysis ultimately improves both the quality and productivity of software organization
that provides feedback to the developers.
Fault Prevention
Fault prevention is an important activity in any software project. Identify the cause of faults and fault
prevention objective is to prevent them from recurring. Fault Prevention had suffered in the past to analyze the
faults and faults in the future to prevent the occurrence of these types include special operations. Fault
prevention software process to improve the quality of one or more phases of the software life cycle can be
applied.
The benefits of analysis software faults and failures are widely recognized. However, a detailed study
based on concrete data is rare. M Hamill et al. [12] analyze the fault and failure data from two large, real-world
case studies. They specifically discuss the lead of software failure using localization of faults and different faults
due to distribution. The results show that individual faults are caused often distributed through multiple errors in
the entire system. This observation is important because it does not support multiple uses heuristics and
assumptions about the past. Moreover, it is clear that the search for and fixing errors, such software errors that
result in large, complex systems are often in spite of the advances in software development difficult and
challenging tasks.
IV. Faults Prevention Benefits And Limitations
Fault prevention strategies exist, but reflect a high level of test maturity discipline associated with the
testing effort represents the most cost-effective expenditure. To detect errors in the development life cycle from
design to implement code specifications require that helps to prevent the escape of errors. Therefore, test
strategies can be classified into two different categories as, fault detection technologies and fault prevention
technologies.
Fault prevention efforts over a period of application development provide major cost and time savings.
Thus it is also important, reduces the number of faults for reconstruction brings cost reduction, it is easy to
maintain port and reuse makes. It is also necessary for the organization to develop high-quality systems in less
time and provides resources, makes the system reliable. Faults which in turn increases productivity preventive
measures are identified, based on which they have been injected to the life cycle stage can be traced back. A
corrective measure for the promotion of knowledge of lessons learned between projects is a mechanism.
The lack of specific domain knowledge, where new and diverse domain software is a need to develop
and implement. In many occasions, appropriate quality requirements specified are not in the first place. The
inspection operation is labour intensive and requires high skill. Sometimes well-developed quality measurement
may not have been identified at design time.
V. Related Works
No single software fault detection technique is capable of addressing all concerns in error detection.
Similar software reviews and testing, static analysis tools (or automated static analysis) can be used to remove
faults before a software product release. Inspection, prototyping, testing and proofs of correctness are several
approaches to identify faults. Formal inspections to identify faults in the early stages of developing the most
effective and expensive quality assurance techniques. Prototype through several requirements clearly helps to
overcome the faults which are understood. Testing is one of the least effective techniques. May escape detection
in the early stages, which is to blame, those tests could be detected in time. The accuracy proofs especially on
the coding level are a good means of detection. Accuracy in manufacturing the most effective and economical
way of building software.
J Zhang et al. [13] determine the extent to which automated static analysis can in the economic
production to help a high quality product, they have static analysis and examine errors and customer reported
losses for the three major in developed industrial software systems analyzed at Nortel Networks, The data show
that automated static analysis is an affordable means of software error detection. Using orthogonal defect
classification scheme, they found that automated static analysis effectively in identifying and mapping error
checking, so that subsequent software production phases to focus on more complex, functional and algorithmic
error. Much of the shortcomings that seem determined by automated static analysis are produced by a few major
types of programming errors and some of these types have the potential to cause security vulnerabilities.
5. A Review on Software Fault Detection and Prevention Mechanism in Software Development Activities
DOI: 10.9790/0661-17652530 www.iosrjournals.org 29 | Page
Statistical analysis results indicate the number of automated static analysis errors can be effective for identifying
modules problem. The results analysis shows that the static analysis tools complement other error detection
techniques for the economical production of high-quality software product.
Khoshgovar and Allen [14], [15] have proposed a model to check for software quality factors such as
future fault density modules list. The inputs to the model are software complexity metrics such as LOC, number
of unique operators and complexity. A stepwise regression is then performed to find weights for each factor.
Briand et al.[16] using object-oriented metrics to predict classes that are likely to contain faults and used PCA in
combination with logistic regression to predict failure-prone classes. Morasca and Ruhe [17] predicts risky
faults modules using rough set theory and logistic regression in commercial software.
Over the years, several software techniques have been developed to support log-based fault analysis,
the integration of state-of-the art gathering techniques to manipulate and to model the log data, for example
MEADEP [18], Analyzing NOW [19], and SEC [20], [21]. However, log-based analysis is not supported by
fully automated procedures so that most of the processing loads to analysts log is the often limited knowledge
about the system. For instance, the authors in [22] have defined a complex algorithm for OS reboots from the
log to identify on the basis of sequential analysis of log messages. Moreover, since an error activating multiple
messages in the log cause a considerable effort to spent to the entries on the same mistake manifestation merged
results [23], [24], [25]. Pre-processing tasks are critical to obtaining accurate failure analysis [26], [27].
While many case studies in the failure prediction in application for industry records reported [28], [29],
[30] few studies have estimated achieved through early fault detection to reduce the test effort or increase the
software quality. Li et al. [31] reported experience of application field fault prediction in ABB Inc. Their
experiences are practical questions about how to select a suitable modelling method and how to evaluate the
accuracy of the forecasts for several releases in the time period. They evaluated the usefulness of forecasts based
on expert opinions. They reported that modules are identified vulnerable by experts as the failures of the top
four fault prone identifies modules of the prediction model. They also reported that the module prioritization
results were actually used by a test team to uncover the original be the low fault-prone additional faults in a
module. Unfortunately it has no quantitative information on the effort for additional testing and the number of
uncovered additional deficiencies required.
Mende and Koschke [32] and Kamei et. al [33] suggested that the efforts consciously measure to assess
the failure prediction accuracy. While conventional valuation measures such as recall, precision, Alberg charts
and ROC curves ignore the cost of quality assurance takes its action, the audit or review of a module is roughly
expected to be proportional to the size. They took the advantage of their measure to the bottom to find the
required prediction accuracy is required for the real testing.
C F. Kemerer et al. [34] studied the influence of the checking rate on software quality, while the
controller for a comprehensive range of factors that can affect the analysis. The data comes from the Personal
Software Process (PSP), which implements carried out inspections, the development group activities. In
particular, the PSP design and code review rates correspond to the preparatory courses in inspections.
VI. Conclusion
Today there is an inherent need for software reliability is getting increased attention these days and
highly fault tolerant system. In this survey paper, research on fault detection mechanism, as well as fault
prevention mechanism in relation to the recent trend of the latest technologies have been discussed. There flaw
detection and software systems used to diagnose the vast number of methods and techniques, but not every tech
suits every system. Select technology system arrangement, size and complexity of adaptability and reliability
targets, technology platform, driven by critical factors. Automated way to detect a tendency to higher levels in
hybrid mining techniques and statistical models are in leaning toward more traditional systems-oriented
solutions for diagnostics and prevention. Fault handling in modern day applications are in the early stages of
research and the solution architecture try to build tolerance level as much as possible.
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