This document discusses statistical quality assurance techniques for software. It explains that statistical quality assurance involves collecting information about software errors, categorizing them, tracing each error to its underlying cause using the Pareto principle to identify the most common causes, and correcting problems that led to those errors. Common causes of software defects are then listed, such as incomplete specifications, misinterpreted customer communication, and violations of programming standards. The document states that statistical quality assurance techniques have been shown to significantly improve software quality at some organizations through around a 50% reduction in defects per year. It concludes by introducing Six Sigma as a widely used strategy for statistical quality assurance, originally developed by Motorola, which uses data analysis to measure and improve processes by identifying and eliminating
The document discusses formal approaches to software quality assurance (SQA). It states that SQA can be improved through software engineering practices like technical reviews, multi-tiered testing, controlling work products and changes, and following standards. It also argues that a more rigorous mathematical approach is needed for SQA since programs can be viewed as mathematical objects with rigorous syntax and semantics defined for languages, allowing proofs of correctness.
The document describes the waterfall model of software development. It begins by listing the presenters and defining sequential and incremental software development models. It then discusses the waterfall model in more detail, describing it as a linear sequential process where each phase must be completed before the next begins. The document outlines the history, use cases, diagram, phases and advantages/disadvantages of the waterfall model.
This ppt covers the following
A strategic approach to testing
Test strategies for conventional software
Test strategies for object-oriented software
Validation testing
System testing
The art of debugging
Importance & Principles of Modeling from UML DesigningABHISHEK KUMAR
ย
Object oriented analysis and design uses modeling to understand systems being developed. Models simplify systems at different abstraction levels to visualize structure and behavior, provide templates for building systems, and document decisions. Effective modeling requires choosing appropriate models that influence solutions, expressing models at different abstraction levels for different stakeholders, ensuring models are grounded in reality, and using multiple complementary models to solve complex systems.
This document outlines the 10 step process for step-wise project planning for software projects. The 10 steps are: 1) identify project scope and objectives, 2) identify project infrastructure, 3) analyze project characteristics, 4) identify project products and activities, 5) estimate effort for each activity, 6) identify activity risks, 7) allocate resources, 8) review and publicize the plan, 9) execute the plan, and 10) conduct lower level planning as more details emerge. The goal of these steps is to ensure project tasks are well coordinated and meet objectives like timely completion through processes like stakeholder analysis, risk assessment, and resource planning.
Flow-oriented modeling represents how data objects are transformed as they move through a system. A data flow diagram (DFD) is the diagrammatic form used to depict this approach. DFDs show the flow of data through processes and external entities of a system using symbols like circles and arrows. They provide a unique view of how a system works by modeling the input, output, storage and processing of data from level to level.
The document discusses formal approaches to software quality assurance (SQA). It states that SQA can be improved through software engineering practices like technical reviews, multi-tiered testing, controlling work products and changes, and following standards. It also argues that a more rigorous mathematical approach is needed for SQA since programs can be viewed as mathematical objects with rigorous syntax and semantics defined for languages, allowing proofs of correctness.
The document describes the waterfall model of software development. It begins by listing the presenters and defining sequential and incremental software development models. It then discusses the waterfall model in more detail, describing it as a linear sequential process where each phase must be completed before the next begins. The document outlines the history, use cases, diagram, phases and advantages/disadvantages of the waterfall model.
This ppt covers the following
A strategic approach to testing
Test strategies for conventional software
Test strategies for object-oriented software
Validation testing
System testing
The art of debugging
Importance & Principles of Modeling from UML DesigningABHISHEK KUMAR
ย
Object oriented analysis and design uses modeling to understand systems being developed. Models simplify systems at different abstraction levels to visualize structure and behavior, provide templates for building systems, and document decisions. Effective modeling requires choosing appropriate models that influence solutions, expressing models at different abstraction levels for different stakeholders, ensuring models are grounded in reality, and using multiple complementary models to solve complex systems.
This document outlines the 10 step process for step-wise project planning for software projects. The 10 steps are: 1) identify project scope and objectives, 2) identify project infrastructure, 3) analyze project characteristics, 4) identify project products and activities, 5) estimate effort for each activity, 6) identify activity risks, 7) allocate resources, 8) review and publicize the plan, 9) execute the plan, and 10) conduct lower level planning as more details emerge. The goal of these steps is to ensure project tasks are well coordinated and meet objectives like timely completion through processes like stakeholder analysis, risk assessment, and resource planning.
Flow-oriented modeling represents how data objects are transformed as they move through a system. A data flow diagram (DFD) is the diagrammatic form used to depict this approach. DFDs show the flow of data through processes and external entities of a system using symbols like circles and arrows. They provide a unique view of how a system works by modeling the input, output, storage and processing of data from level to level.
This ppt describes about the bug.. and how bugs provide clues for debugging in software? , What will be consider in debugging a software? Debugging strategies and automatic debugging. The ppt prepared based on examination point of view for software engineering subject
The data design action translates data objects into data structures at the software component level.
Data Design is the first and most important design activity. Here the main issue is to select the appropriate data structure i.e. the data design focuses on the definition of data structures.
Data design is a process of gradual refinement, from the coarse "What data does your application require?" to the precise data structures and processes that provide it. With a good data design, your application's data access is fast, easily maintained, and can gracefully accept future data enhancements.
presentation on design of a 2 pass assembler, and variant I and variant II in the subject of systems programming. especially helpful to GTU students, CSE and IT engineers
1) Software reliability models estimate the defect rate and quality of software either through static attributes or dynamic testing patterns.
2) Dynamic models like the Rayleigh and Weibull distributions use statistical analysis of defect patterns over time to project future reliability. Finding and removing defects earlier in the development process leads to better quality in later stages.
3) Accuracy of estimates from reliability models depends on the input data and how well the model fits the specific organization. No single model works for all situations.
This document discusses software project management artifacts. Artifacts are organized into management and engineering sets. The management set includes artifacts like the work breakdown structure, business case, and software development plan. The engineering set includes requirement, design, implementation, and deployment artifact sets. Each set captures information through various notations and tools to manage the software development lifecycle.
This document discusses software reliability models and the Rayleigh model in particular. It explains that reliability models can be static or dynamic, and the Rayleigh model is a dynamic model based on a Weibull distribution. The Rayleigh model uses parameters estimated from project data to project defect rates. Higher defect rates during development generally correlate with higher field defect rates. More defects found and removed earlier in the process yields better quality. Accuracy of models depends on valid input data and establishing predictive validity for different organizations.
Quality Management in Software Engineering SE24koolkampus
ย
This document discusses quality management in software development. It covers quality assurance and standards, quality planning, quality control, software measurement and metrics. Quality management aims to ensure the required level of quality is achieved in software products by defining quality standards and procedures and making quality everyone's responsibility. Standards are key to effective quality management as they encapsulate best practices and provide a framework for quality assurance processes. Quality reviews and software measurement are important for quality control.
The document discusses context models and their use in system modeling. Context models illustrate the operational context of a system by showing what lies outside its boundaries, including other systems in the environment. They help define a system's boundaries and show how IT applications fit into the context of people and organizations. Two examples are provided: (1) a Mental Health Care Patient Management System (MHC-PMS) and its connections to other clinical systems; (2) an Automated Teller Machine (ATM) and its links to banking systems. Context models on their own do not show relationships between external systems, so additional models are needed.
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.
The document discusses different models for distributed systems including physical, architectural and fundamental models. It describes the physical model which captures the hardware composition and different generations of distributed systems. The architectural model specifies the components and relationships in a system. Key architectural elements discussed include communicating entities like processes and objects, communication paradigms like remote invocation and indirect communication, roles and responsibilities of entities, and their physical placement. Common architectures like client-server, layered and tiered are also summarized.
The document discusses issues with the conventional "waterfall" model of software development and proposes improvements. It analyzes that the waterfall model is risky and invites failure due to late testing exposing design flaws. It then provides 5 necessary improvements: 1) adding a design phase before analysis, 2) increased documentation, 3) developing the software in two iterations, 4) improved testing planning and 5) increased customer involvement. It also discusses common issues seen in practice with the waterfall model like protracted integration, late risk resolution, and adversarial stakeholder relationships due to a focus on documents over working software.
The document discusses component-level design which occurs after architectural design. It aims to create a design model from analysis and architectural models. Component-level design can be represented using graphical, tabular, or text-based notations. The key aspects covered include:
- Defining a software component as a modular building block with interfaces and collaboration
- Designing class-based components following principles like open-closed and dependency inversion
- Guidelines for high cohesion and low coupling in components
- Designing conventional components using notations like sequence, if-then-else, and tabular representations
Peephole optimization techniques in compiler designAnul Chaudhary
ย
This document discusses various compiler optimization techniques, focusing on peephole optimization. It defines optimization as transforming code to run faster or use less memory without changing functionality. Optimization can be machine-independent, transforming code regardless of hardware, or machine-dependent, tailored to a specific architecture. Peephole optimization examines small blocks of code and replaces them with faster or smaller equivalents using techniques like constant folding, strength reduction, null sequence elimination, and algebraic laws. Common replacement rules aim to improve performance, reduce memory usage, and decrease code size.
The document discusses various techniques for debugging software bugs, including gathering relevant information, forming and testing hypotheses about the cause, and strategies like tracing execution, simplifying tests, questioning assumptions, and cleaning up unused code. It also provides a checklist for determining the root cause of bugs and ensuring debugging efforts are focused on the right location. The goal of debugging is to understand why bugs occur so they can be removed and prevent future bugs through improved testing, risk management, and learning from past issues.
The document contains slides from a lecture on software engineering. It discusses definitions of software and software engineering, different types of software applications, characteristics of web applications, and general principles of software engineering practice. The slides are copyrighted and intended for educational use as supplementary material for a textbook on software engineering.
This document provides an overview of advanced software engineering and software process improvement (SPI). It discusses SPI frameworks like the Capability Maturity Model (CMM) and defines what SPI entails. The document outlines the five activities in the SPI process: assessment and gap analysis, education and training, selection and justification, installation/migration, and evaluation. It also discusses SPI risks, success factors, maturity models, and returns on investment. Finally, it covers the People CMM and trends toward more agile SPI approaches.
Software re-engineering is a process of examining and altering a software system to restructure it and improve maintainability. It involves sub-processes like reverse engineering, redocumentation, and data re-engineering. Software re-engineering is applicable when some subsystems require frequent maintenance and can be a cost-effective way to evolve legacy software systems. The key advantages are reduced risk compared to new development and lower costs than replacing the system entirely.
The document discusses software quality assurance (SQA) and defines key terms related to quality. It describes SQA as encompassing quality management, software engineering processes, formal reviews, testing strategies, documentation control, and compliance with standards. Specific SQA activities mentioned include developing an SQA plan, participating in process development, auditing work products, and ensuring deviations are addressed. The document also discusses software reviews, inspections, reliability, and the reliability specification process.
Advanced Methods for Outlier Detection and Analysis in Semiconductor Manufact...yieldWerx Semiconductor
ย
Advanced methods for outlier detection are important for semiconductor manufacturing. There are three main categories of outlier detection: statistical process control using control charts, supervised learning using labeled training data, and unsupervised learning using unlabeled data to find unknown anomalies. Part average testing and dynamic part average testing are key tools that compare individual devices to averages to find outliers. Outlier analysis plays a critical role in semiconductor yield monitoring systems by providing insights into process deviations affecting manufacturing yield. Semiconductor data is crucial for effective analytics and outlier detection.
See how IT Risks Impacts your Business. CAST help you to check on software performance, stability, maintainability, and security vulnerabilities in which CAST excels and successfully differentiates from code analyzers.CASTโs Application Intelligence Platform and Rapid Portfolio Analysis solutions can help you avoid these types of โsoftware glitchesโ or "software risks" by allowing you to gain greater visibility through automated code review that identifies the root causes of risks before they become production problems, while expediting time-to-market with shorter release time lines and improved business agility.
This ppt describes about the bug.. and how bugs provide clues for debugging in software? , What will be consider in debugging a software? Debugging strategies and automatic debugging. The ppt prepared based on examination point of view for software engineering subject
The data design action translates data objects into data structures at the software component level.
Data Design is the first and most important design activity. Here the main issue is to select the appropriate data structure i.e. the data design focuses on the definition of data structures.
Data design is a process of gradual refinement, from the coarse "What data does your application require?" to the precise data structures and processes that provide it. With a good data design, your application's data access is fast, easily maintained, and can gracefully accept future data enhancements.
presentation on design of a 2 pass assembler, and variant I and variant II in the subject of systems programming. especially helpful to GTU students, CSE and IT engineers
1) Software reliability models estimate the defect rate and quality of software either through static attributes or dynamic testing patterns.
2) Dynamic models like the Rayleigh and Weibull distributions use statistical analysis of defect patterns over time to project future reliability. Finding and removing defects earlier in the development process leads to better quality in later stages.
3) Accuracy of estimates from reliability models depends on the input data and how well the model fits the specific organization. No single model works for all situations.
This document discusses software project management artifacts. Artifacts are organized into management and engineering sets. The management set includes artifacts like the work breakdown structure, business case, and software development plan. The engineering set includes requirement, design, implementation, and deployment artifact sets. Each set captures information through various notations and tools to manage the software development lifecycle.
This document discusses software reliability models and the Rayleigh model in particular. It explains that reliability models can be static or dynamic, and the Rayleigh model is a dynamic model based on a Weibull distribution. The Rayleigh model uses parameters estimated from project data to project defect rates. Higher defect rates during development generally correlate with higher field defect rates. More defects found and removed earlier in the process yields better quality. Accuracy of models depends on valid input data and establishing predictive validity for different organizations.
Quality Management in Software Engineering SE24koolkampus
ย
This document discusses quality management in software development. It covers quality assurance and standards, quality planning, quality control, software measurement and metrics. Quality management aims to ensure the required level of quality is achieved in software products by defining quality standards and procedures and making quality everyone's responsibility. Standards are key to effective quality management as they encapsulate best practices and provide a framework for quality assurance processes. Quality reviews and software measurement are important for quality control.
The document discusses context models and their use in system modeling. Context models illustrate the operational context of a system by showing what lies outside its boundaries, including other systems in the environment. They help define a system's boundaries and show how IT applications fit into the context of people and organizations. Two examples are provided: (1) a Mental Health Care Patient Management System (MHC-PMS) and its connections to other clinical systems; (2) an Automated Teller Machine (ATM) and its links to banking systems. Context models on their own do not show relationships between external systems, so additional models are needed.
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.
The document discusses different models for distributed systems including physical, architectural and fundamental models. It describes the physical model which captures the hardware composition and different generations of distributed systems. The architectural model specifies the components and relationships in a system. Key architectural elements discussed include communicating entities like processes and objects, communication paradigms like remote invocation and indirect communication, roles and responsibilities of entities, and their physical placement. Common architectures like client-server, layered and tiered are also summarized.
The document discusses issues with the conventional "waterfall" model of software development and proposes improvements. It analyzes that the waterfall model is risky and invites failure due to late testing exposing design flaws. It then provides 5 necessary improvements: 1) adding a design phase before analysis, 2) increased documentation, 3) developing the software in two iterations, 4) improved testing planning and 5) increased customer involvement. It also discusses common issues seen in practice with the waterfall model like protracted integration, late risk resolution, and adversarial stakeholder relationships due to a focus on documents over working software.
The document discusses component-level design which occurs after architectural design. It aims to create a design model from analysis and architectural models. Component-level design can be represented using graphical, tabular, or text-based notations. The key aspects covered include:
- Defining a software component as a modular building block with interfaces and collaboration
- Designing class-based components following principles like open-closed and dependency inversion
- Guidelines for high cohesion and low coupling in components
- Designing conventional components using notations like sequence, if-then-else, and tabular representations
Peephole optimization techniques in compiler designAnul Chaudhary
ย
This document discusses various compiler optimization techniques, focusing on peephole optimization. It defines optimization as transforming code to run faster or use less memory without changing functionality. Optimization can be machine-independent, transforming code regardless of hardware, or machine-dependent, tailored to a specific architecture. Peephole optimization examines small blocks of code and replaces them with faster or smaller equivalents using techniques like constant folding, strength reduction, null sequence elimination, and algebraic laws. Common replacement rules aim to improve performance, reduce memory usage, and decrease code size.
The document discusses various techniques for debugging software bugs, including gathering relevant information, forming and testing hypotheses about the cause, and strategies like tracing execution, simplifying tests, questioning assumptions, and cleaning up unused code. It also provides a checklist for determining the root cause of bugs and ensuring debugging efforts are focused on the right location. The goal of debugging is to understand why bugs occur so they can be removed and prevent future bugs through improved testing, risk management, and learning from past issues.
The document contains slides from a lecture on software engineering. It discusses definitions of software and software engineering, different types of software applications, characteristics of web applications, and general principles of software engineering practice. The slides are copyrighted and intended for educational use as supplementary material for a textbook on software engineering.
This document provides an overview of advanced software engineering and software process improvement (SPI). It discusses SPI frameworks like the Capability Maturity Model (CMM) and defines what SPI entails. The document outlines the five activities in the SPI process: assessment and gap analysis, education and training, selection and justification, installation/migration, and evaluation. It also discusses SPI risks, success factors, maturity models, and returns on investment. Finally, it covers the People CMM and trends toward more agile SPI approaches.
Software re-engineering is a process of examining and altering a software system to restructure it and improve maintainability. It involves sub-processes like reverse engineering, redocumentation, and data re-engineering. Software re-engineering is applicable when some subsystems require frequent maintenance and can be a cost-effective way to evolve legacy software systems. The key advantages are reduced risk compared to new development and lower costs than replacing the system entirely.
The document discusses software quality assurance (SQA) and defines key terms related to quality. It describes SQA as encompassing quality management, software engineering processes, formal reviews, testing strategies, documentation control, and compliance with standards. Specific SQA activities mentioned include developing an SQA plan, participating in process development, auditing work products, and ensuring deviations are addressed. The document also discusses software reviews, inspections, reliability, and the reliability specification process.
Advanced Methods for Outlier Detection and Analysis in Semiconductor Manufact...yieldWerx Semiconductor
ย
Advanced methods for outlier detection are important for semiconductor manufacturing. There are three main categories of outlier detection: statistical process control using control charts, supervised learning using labeled training data, and unsupervised learning using unlabeled data to find unknown anomalies. Part average testing and dynamic part average testing are key tools that compare individual devices to averages to find outliers. Outlier analysis plays a critical role in semiconductor yield monitoring systems by providing insights into process deviations affecting manufacturing yield. Semiconductor data is crucial for effective analytics and outlier detection.
See how IT Risks Impacts your Business. CAST help you to check on software performance, stability, maintainability, and security vulnerabilities in which CAST excels and successfully differentiates from code analyzers.CASTโs Application Intelligence Platform and Rapid Portfolio Analysis solutions can help you avoid these types of โsoftware glitchesโ or "software risks" by allowing you to gain greater visibility through automated code review that identifies the root causes of risks before they become production problems, while expediting time-to-market with shorter release time lines and improved business agility.
Otto Vinter - Analysing Your Defect Data for Improvement PotentialTEST Huddle
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EuroSTAR Software Testing Conference 2008 presentation on Analysing Your Defect Data for Improvement Potential by Otto Vinter. See more at conferences.eurostarsoftwaretesting.com/past-presentations/
This document discusses 5 key considerations for improving healthcare IT performance and security: 1) Get a handle on assets to improve scalability, 2) Break out of technology silos to improve reliability, 3) Target specific areas for performance improvement, 4) Predict future performance issues related to scalability and performance, and 5) Implement a methodology for continuous improvement rather than reactive changes. It emphasizes focusing on "lead metrics" like packet loss that predict issues rather than "lag metrics" that measure after problems occur.
This document provides an overview of a presentation on software reliability, safety, and testing for industrial applications. The agenda includes introductions on the need for reliable and safe software, challenges and issues, system reliability and failure distribution, hardware vs. software reliability, software reliability prediction models, and software safety and functional safety. The presentation also discusses Dr. Ajeet Kumar's background and credentials, research contributions, invited lectures, and publications.
This document discusses software quality measurement and outlines an ecosystem and objectives for the Center for Information-Driven Quality (CISQ). The objectives are to:
1. Raise awareness of the challenge of IT software quality.
2. Develop standard, automatable measures and anti-patterns for evaluating software quality.
3. Promote global acceptance of quality standards in acquiring software.
4. Develop infrastructure like authorized assessors and conforming products.
From Data to Insights: How IT Operations Data Can Boost QualityCognizant
ย
By leveraging highly-analyzed operational data - the voice of customers, machines and tests - quality assurance (QA) and IT groups can derive major gains in quality of apps and in user experience.
A VISUAL ATTENDANCE SYSTEM USING FACE RECOGNITIONIRJET Journal
ย
This document describes a visual attendance system using face recognition. The system was created to make the traditional paper-based attendance process more efficient and less prone to errors. It uses computer vision and face recognition techniques, including the RetinaFace and Arcnet algorithms, to detect and identify students' faces from video feeds or images taken in the classroom. When taking attendance, the system captures photos of students present and searches its database of student faces to automatically record attendance without disrupting the class. The document discusses the methodology and system structure, including face detection, face recognition modeling, and an overall workflow flowchart. It aims to provide an improved digital solution for tracking attendance at universities and colleges.
A Review on Software Fault Detection and Prevention Mechanism in Software Dev...iosrjce
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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.
This document provides an overview of software fault detection and prevention mechanisms. It discusses several fault detection mechanisms used in the software development lifecycle, including automated static analysis, graph mining, and classifiers. Automated static analysis tools can find standard problems but miss many faults that could lead to failures. Graph mining uses call graph analysis to identify issues in function calling frequencies or structures. Classifiers like NaiveBayes can be trained on normal code behavior to identify abnormal events. The document also discusses fault prevention benefits, related work, and concludes with the importance of fault detection and prevention for developing high quality, reliable software.
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2J5SAcT.
Detlef Nauck explains why the testing of data is essential, as it not only drives the machine learning phase itself, but it is paramount for producing reliable predictions after deployment. Testing the decisions made by a deployed machine learning model is equally important to understand if it delivers the expected business value. Filmed at qconlondon.com.
Detlef Nauck is Chief Research Scientist for Data Science with BT's Research and Innovation Division. He is leading a group of scientists working on research into Data Science, ML and AI. He focuses on establishing best practices in DS for conducting analytics professionally and responsibly leading to new ways of analysing data for achieving better insights.
The semiconductor industry is highly advanced and constantly evolving, with manufacturers always seeking new ways to improve their processes and reduce defects. There are several tools and techniques that are commonly used in the industry to achieve, zero defects or near-zero defect rates.
IRJET- Machine Learning Processing for Intrusion DetectionIRJET Journal
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This document evaluates different machine learning algorithms for network intrusion detection using the KDD dataset. It analyzes the accuracy of logistic regression, naive bayes, support vector machine, K-nearest neighbor, and decision tree classifiers based on their confusion matrices and receiver operating characteristic curves. The results show that the decision tree algorithm achieved the highest accuracy rate of 99.83% on the KDD dataset for intrusion detection.
IRJET- Data Reduction in Bug Triage using Supervised Machine LearningIRJET Journal
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This document discusses using machine learning techniques for automatic bug triage to reduce the time and costs associated with manually assigning software bugs to developers. It proposes using data reduction techniques like feature selection and instance selection to create a smaller, higher quality bug repository by removing redundant bug reports and words. This reduced dataset would then be used to train a classifier to automatically suggest the most suitable developer for a given new bug, aiming to improve prediction accuracy while reducing training and prediction time compared to using the full dataset.
IT General Controls Presentation at IIA Vadodara Audit ClubKaushal Trivedi
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The document discusses threats to information technology systems such as data theft, cyberattacks, and system vulnerabilities. It then provides an overview of information technology general controls (ITGCs) and how they are important for ensuring the secure, stable, and reliable performance of technology systems. Finally, it discusses specific areas of focus for ITGCs such as security management, change management, and testing methodologies.
IRJET- Secure Automated Teller Machine (ATM) by Image ProcessingIRJET Journal
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1) The document discusses using image processing techniques like fingerprint scanning, iris scanning, and face recognition for secure authentication in automated teller machines (ATMs).
2) It analyzes different biometric methods and proposes two algorithms that use biometrics like fingerprints for verification to prevent fraudulent ATM access and reduce attacks on the system.
3) The algorithms are meant to provide a higher level of security compared to existing ATM systems by making the ATM only accessible based on biometric matching, though implementing such a system comes at a higher cost.
ATM fraud detection system using machine learning algorithmsIRJET Journal
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This document presents a machine learning-based system for detecting ATM fraud. It examines current types of ATM fraud and proposes using machine learning algorithms to build a detection system. The study evaluates supervised, unsupervised and semi-supervised machine learning approaches for this task. It specifically explores hidden Markov models, support vector machines and decision trees for fraud detection. The document outlines the methodology, including data collection, model training and testing. Charts and figures show sample outputs from the system, including detected fraud over time. The system is found to effectively detect fraud while minimizing false positives. Machine learning is concluded to be well-suited for ATM fraud detection.
Bhadale group of companies data science project methodologies catalogueVijayananda Mohire
ย
This is our offering for data science project methodologies. We offer our expertise in transforming your enterprise for the next big data revolution for Data science project
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THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...indexPub
ย
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
Information and Communication Technology in EducationMJDuyan
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(๐๐๐ ๐๐๐) (๐๐๐ฌ๐ฌ๐จ๐ง 2)-๐๐ซ๐๐ฅ๐ข๐ฆ๐ฌ
๐๐ฑ๐ฉ๐ฅ๐๐ข๐ง ๐ญ๐ก๐ ๐๐๐ ๐ข๐ง ๐๐๐ฎ๐๐๐ญ๐ข๐จ๐ง:
Students will be able to explain the role and impact of Information and Communication Technology (ICT) in education. They will understand how ICT tools, such as computers, the internet, and educational software, enhance learning and teaching processes. By exploring various ICT applications, students will recognize how these technologies facilitate access to information, improve communication, support collaboration, and enable personalized learning experiences.
๐๐ข๐ฌ๐๐ฎ๐ฌ๐ฌ ๐ญ๐ก๐ ๐ซ๐๐ฅ๐ข๐๐๐ฅ๐ ๐ฌ๐จ๐ฎ๐ซ๐๐๐ฌ ๐จ๐ง ๐ญ๐ก๐ ๐ข๐ง๐ญ๐๐ซ๐ง๐๐ญ:
-Students will be able to discuss what constitutes reliable sources on the internet. They will learn to identify key characteristics of trustworthy information, such as credibility, accuracy, and authority. By examining different types of online sources, students will develop skills to evaluate the reliability of websites and content, ensuring they can distinguish between reputable information and misinformation.
How to Setup Default Value for a Field in Odoo 17Celine George
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In Odoo, we can set a default value for a field during the creation of a record for a model. We have many methods in odoo for setting a default value to the field.
2. Statistical quality assurance implies
the following steps
1. Information about software errors and defects is collected
and categorized.
2. An attempt is made to trace each error and defect to its
underlying cause (e.g., non-conformance to specifications,
design error, violation of standards, poor communication
with the customer).
3. Using the Pareto principle (80 percent of the defects can
be traced to 20 percent of all possible causes), isolate the
20 percent (the vital few).
4. Once the vital few causes have been identified, move to
correct the problems that have caused the errors and
defects
3. A Software may encountered more defects and uncovered
errors
โขIncomplete or erroneous specifications (IES)
โข Misinterpretation of customer communication
(MCC)
โข Intentional deviation from specifications (IDS)
โข Violation of programming standards (VPS)
โข Error in data representation (EDR)
โข Inconsistent component interface (ICI)
โข Error in design logic (EDL)
โข Incomplete or erroneous testing (IET)
โข Inaccurate or incomplete documentation (IID)
โข Error in programming language translation of
design (PLT)
โข Ambiguous or inconsistent human/computer
interface (HCI)
โข Miscellaneous (MIS)
4. Statistical quality assurance techniques for software have been shown to
provide substantial quality improvement [Art97]. In some cases, software
organizations have achieved a 50 percent reduction per year in defects after
applying these
techniques
5. Six Sigma is the most widely used strategy for statistical
quality assurance in industry today. Originally
popularized by Motorola in the 1980s.
Six Sigma strategy โis a rigorous and disciplined
methodology that uses data and statistical analysis to
measure and improve a companyโs operational
performance by identifying and eliminating defectsโ in
manufacturing and service-related processesโ
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