Cukic Promise08 V3
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The study compared software quality prediction models using design metrics, code metrics, and a combination of both, on multiple projects from NASA. The results showed that models using a combination of design and code metrics performed better than using either metric set alone, and code metrics generally performed better than design metrics alone. Combining metrics from different development phases provided better fault prediction than only using code-level metrics.
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Introduction to specification based test design techniques
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- The main challenge is organizational rather than technological - how can we ensure the valuable lessons learned from this work are not wasted?
The Robust Optimization of Non-Linear Requirements Models
The document discusses robust optimization of non-linear requirements models. It introduces the defect detection and prevention (DDP) model used at NASA JPL to represent requirements, risks, mitigations, and their relationships. It then describes two algorithms, KEYS and KEYS2, for efficiently exploring the search space of the DDP model to find high-quality, robust solutions. Finally, it benchmarks these algorithms against other search-based approaches like simulated annealing, MaxWalkSat, and A* search.
Unit 6
This document discusses various metrics for measuring software quality and object-oriented design. It introduces McCall's quality factors triangle and describes measures, metrics, and indicators. It then discusses principles of software measurement and the measurement process. It provides examples of function-based metrics, architectural design metrics, object-oriented design metrics, and class-oriented metrics. The document aims to define different metrics and provide guidance on applying them to assess software quality.
14 software technical_metrics
The document discusses software technical metrics, including McCall's quality factors and architectural design metrics. [1] McCall identified 11 quality factors like correctness, reliability, and usability that can measure software quality. [2] These factors depend on 22 metrics that can be subjectively measured on a scale from 0 to 10. [3] The document also discusses architectural structure metrics like Fenton's size and connectivity density metrics, which measure attributes like nodes and arcs.
Software Metrics
This document provides an overview and introduction to software metrics. It discusses measurement concepts and why measurement is important for software engineering. It covers topics like the basics of measurement, collecting metrics data, analyzing data, and measuring internal and external attributes of software. Specific metrics discussed include size, structure, complexity, reliability, and test coverage. The document is intended to introduce readers to fundamental software metrics concepts.
Software 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.
Software design metrics
This is presentation where i have expresed my thought on Software Metrics which could improve software quality reach me for further discussions
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21st century effort estimation methods from JPL. Jairus Hihn (JPL), Tim Menzies (WVU), Karen Lum (JPL), Dan Baker (WVU)
Software testing effort estimation with cobb douglas function- a practical ap...
Abstract Effort estimation is one of the critical challenges in Software Testing Life Cycle (STLC). It is the basis for the project’s effort estimation, planning, scheduling and budget planning. This paper illustrates model with an objective to depict the accuracy and bias variation of an organization’s estimates of software testing effort through Cobb-Douglas function (CDF). Data variables selected for building the model were believed to be vital and have significant impact on the accuracy of estimates. Data gathered for the completed projects in the organization for about 13 releases. Statistically, all variables in this model were statistically significant at p<0.05><0.01 levels. The Cobb-Douglas function was selected and used for the software testing effort estimation. The results achieved with CDF were compared with the estimates provided by the area expert. The model’s estimation figures are more accurate than the expert judgment. CDF has one of the appropriate techniques for estimating effort for software testing. CDF model accuracy is 93.42%.
A Validation of Object-Oriented Design Metrics as Quality Indicators
The document summarizes a research paper that empirically validated several object-oriented design metrics proposed by Chidamber and Kemerer as indicators of fault-prone classes. The study analyzed 6 metrics on 180 classes from a system. Univariate analysis found 5 metrics to be significantly correlated with fault probability. Multivariate analysis using these 5 metrics achieved better prediction of faulty classes than models using traditional code metrics. The research validated that these OO design metrics can help identify fault-prone classes early in the development lifecycle.
A Hierarchical Feature Set optimization for effective code change based Defec...
This document summarizes research on using support vector machines (SVMs) for software defect prediction. It analyzes 11 datasets from NASA projects containing code metrics and defect information for modules. The researchers preprocessed the data by removing duplicate/inconsistent instances, constant attributes, and balancing the datasets. They used SVMs with 5-fold cross validation to classify modules as defective or non-defective, achieving an average accuracy of 70% across the datasets. The researchers conclude SVMs can effectively predict defects but note earlier studies using the NASA data may have overstated capabilities due to insufficient data preprocessing.
A value added predictive defect type distribution model
distribution model of software quality Assurance and management .
Reference is https://ieeexplore.ieee.org/document/4529863/
software engineering module i & ii.pptx
The document discusses various metrics that can be used to measure different aspects of software products and processes. It describes McCall's quality factors for software products and defines measures, metrics, and indicators. It also outlines principles for software measurement, the measurement process, and goal-oriented measurement using the Goal/Question/Metric paradigm. Finally, it discusses different types of metrics including product, process, project, quality, functional, size, and object-oriented design metrics.
Towards a Better Understanding of the Impact of Experimental Components on De...
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Software Quality Assurance (SQA) teams play a critical role in the software development process to ensure the absence of software defects. It is not feasible to perform exhaustive SQA tasks (i.e., software testing and code review) on a large software product given the limited SQA resources that are available. Thus, the prioritization of SQA efforts is an essential step in all SQA efforts. Defect prediction models are used to prioritize risky software modules and understand the impact of software metrics on the defect-proneness of software modules. The predictions and insights that are derived from defect prediction models can help software teams allocate their limited SQA resources to the modules that are most likely to be defective and avoid common past pitfalls that are associated with the defective modules of the past. However, the predictions and insights that are derived from defect prediction models may be inaccurate and unreliable if practitioners do not control for the impact of experimental components (e.g., datasets, metrics, and classifiers) on defect prediction models, which could lead to erroneous decision-making in practice. In this thesis, we investigate the impact of experimental components on the performance and interpretation of defect prediction models. More specifically, we investigate the impact of the three often overlooked experimental components (i.e., issue report mislabelling, parameter optimization of classification techniques, and model validation techniques) have on defect prediction models. Through case studies of systems that span both proprietary and open-source domains, we demonstrate that (1) issue report mislabelling does not impact the precision of defect prediction models, suggesting that researchers can rely on the predictions of defect prediction models that were trained using noisy defect datasets; (2) automated parameter optimization for classification techniques substantially improve the performance and stability of defect prediction models, as well as they change their interpretation, suggesting that researchers should no longer shy from applying parameter optimization to their models; and (3) the out-of-sample bootstrap validation technique produces a good balance between bias and variance of performance estimates, suggesting that the single holdout and cross-validation families that are commonly-used nowadays should be avoided.A Tale of Experiments on Bug Prediction
This document summarizes Martin Pinzger's research on predicting buggy methods using software repository mining. The key points are:
1. Pinzger and colleagues conducted experiments on 21 Java projects to predict buggy methods using source code and change metrics. Change metrics like authors and method histories performed best with up to 96% accuracy.
2. Predicting buggy methods at a finer granularity than files can save manual inspection and testing effort. Accuracy decreases as fewer methods are predicted but change metrics maintain higher precision.
3. Case studies on two classes show that method-level prediction achieves over 82% precision compared to only 17-42% at the file level. This demonstrates the benefit of finer-
Software Defect Prediction on Unlabeled Datasets
The document describes techniques for software defect prediction when labeled training data is unavailable. It proposes Transfer Defect Learning (TCA+) to improve cross-project defect prediction by normalizing data distributions between source and target projects. For projects with heterogeneous metrics, it introduces Heterogeneous Defect Prediction (HDP) which matches similar metrics between source and target to build cross-project prediction models. It also discusses CLAMI for defect prediction using only unlabeled data without human effort. The techniques are evaluated on open source projects to demonstrate their effectiveness compared to traditional cross-project and within-project prediction.
QUALITY METRICS OF TEST SUITES IN TESTDRIVEN DESIGNED APPLICATIONS
New techniques for writing and developing software have evolved in recent years. One is Test-Driven
Development (TDD) in which tests are written before code. No code should be written without first having
a test to execute it. Thus, in terms of code coverage, the quality of test suites written using TDD should be
high.
In this work, we analyze applications written using TDD and traditional techniques. Specifically, we
demonstrate the quality of the associated test suites based on two quality metrics: 1) structure-based
criterion, 2) fault-based criterion. We learn that test suites with high branch test coverage will also have
high mutation scores, and we especially reveal this in the case of TDD applications. We found that TestDriven
Development is an effective approach that improves the quality of the test suite to cover more of the
source code and also to reveal more.
Software testing defect prediction model a practical approach
Abstract Software defects prediction aims to reduce software testing efforts by guiding the testers through the defect classification of software systems. Defect predictors are widely used in many organizations to predict software defects in order to save time, improve quality, testing and for better planning of the resources to meet the timelines. The application of statistical software testing defect prediction model in a real life setting is extremely difficult because it requires more number of data variables and metrics and also historical defect data to predict the next releases or new similar type of projects. This paper explains our statistical model, how it will accurately predict the defects for upcoming software releases or projects. We have used 20 past release data points of software project, 5 parameters and build a model by applying descriptive statistics, correlation and multiple linear regression models with 95% confidence intervals (CI). In this appropriate multiple linear regression model the R-square value was 0.91 and its Standard Error is 5.90%. The Software testing defect prediction model is now being used to predict defects at various testing projects and operational releases. We have found 90.76% precision between actual and predicted defects.
Similar Characteristics of Internal Software Quality Attributes for Object-Or...
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This document discusses a study that used cluster analysis to identify structural similarities between different software domains based on object-oriented metrics. The study analyzed over 150 open-source Java projects across different domains. The results showed that some domains tended to have similar characteristics related to abstraction, stability, complexity, and specialization. For example, security and utility software tended to have few abstract classes and descendants, while games and system administration software had more stable classes. The findings provide insights into structural aspects of object-oriented development across domains.Software engineering Course module
This document outlines the course plan for Software Engineering at Punjab College of Technical Education. The course is divided into 4 sections - Introduction to software engineering, Software project planning, Software design, and Testing. It will include 4 tests and 3 assignments. Topics covered include software characteristics, components, applications, process models like waterfall and spiral, project management, system analysis, design methodologies, and testing strategies. The assessment will include tests, assignments, presentations, and a mid-semester exam, with the internal evaluation totaling 40 marks.
Csqe sample exam 1 solutions 05.00.04
The document provides sample exam questions and answers for the Certified Software Quality Engineer (CSQE) exam. It contains 10 multiple choice questions about software quality topics like regression testing, requirements, stakeholder management, change impact analysis, metrics, risk identification, project budget variances, reproducible builds, and process audits. Each question is followed by an explanation of the correct answer referencing sections of the CSQE Handbook.
What's new in Design-Expert version 9?
Illustrated highlights of new features in Design-Expert 9
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PM3 ARTICALS
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dxDOE design of experiment for students.ppt
Text on statistics which can be used by students and professionals. This covers more topics which are relevant to professionals in the field who need the knowledge"
Statistics is not just a subject confined to textbooks; it's a powerful tool that permeates every aspect of our lives. Whether you're a student embarking on your academic journey or a seasoned professional navigating the complexities of your field, a solid understanding of statistics is indispensable. That's where this comprehensive text comes in.
From the foundational principles to advanced techniques, this text is designed to equip both students and professionals with the knowledge and skills necessary to harness the full potential of statistics. We start by laying the groundwork with essential concepts such as probability theory, random variables, and descriptive statistics. Through clear explanations and illustrative examples, we ensure that readers grasp these fundamental building blocks with ease.
But statistics is not just about crunching numbers; it's about making sense of data and drawing meaningful insights. That's why we delve into inferential statistics, exploring hypothesis testing, confidence intervals, and regression analysis. By learning how to infer conclusions from sample data, readers gain the ability to make informed decisions and predictions based on statistical evidence.
But the journey doesn't stop there. We go beyond the basics to cover advanced topics that are crucial for professionals in today's data-driven world. Multivariate analysis, time series analysis, and Bayesian statistics are just a few of the advanced techniques that readers will master, providing them with the tools to tackle complex problems and extract deeper insights from data.
What sets this text apart is its emphasis on real-world relevance. Each chapter is carefully crafted to bridge the gap between theory and practice, with practical examples and case studies drawn from a wide range of industries and disciplines. Whether you're working in finance, healthcare, marketing, or any other field, you'll find that the principles and techniques covered in this text are directly applicable to your day-to-day work.
Moreover, we recognize that proficiency in statistical software is essential for modern professionals. That's why we include discussions on popular tools such as R, Python, and SPSS, empowering readers to analyze data efficiently and effectively. With hands-on exercises and tutorials, readers can develop their skills in data analysis and visualization, gaining practical experience that will serve them well in their careers.
In sum, this text is more than just a book; it's a comprehensive guide to mastering the art and science of statistics. Whether you're a student seeking to build a strong foundation or a professional looking to expand your analytical toolkit, this text has everything you need to succeed in today's data-driven world. With its clear explanations, practical examples.
Debug me
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Size matters a lot rick collins - technomics
Rick Collins presented on the importance of software size in cost estimating for defense programs. Recent studies by the GAO and others found that many defense programs experienced significant software size growth compared to initial estimates, with actual sizes being 20-60% larger than estimated. This consistent size growth across programs emphasizes the need for cost estimates to be based on actual size data from completed projects in order to establish credible and defensible estimates. Failing to account for likely size growth can lead to unrealistic budgets and schedules for new programs.
Slides chapter 15
The document discusses various metrics that can be used to measure different aspects of software quality. It describes McCall's quality factors triangle which identifies key attributes like correctness, reliability, efficiency etc. It then discusses different types of metrics like function-based metrics which measure functionality, design metrics which measure complexity, and class-oriented metrics which measure characteristics of object-oriented design like coupling and cohesion. The document provides examples of metrics that can measure code, interfaces, testing and more.
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Practical use of defect detection and prediction
Sun Microsystems implemented a defect prediction model using historical bug and escalation data to identify bugs likely to escalate and be proactively fixed. This initially saw successes with 41 patches preventing escalations and notable fixes. However, the model declined over time due to lack of resources, expertise, and executive support. Lessons included ring-fencing resources, keeping the model simple, and continually improving it. Now Sun focuses on regular release criteria and incoming bug triage to identify bugs to proactively fix. They are considering upgrading tools and run-time checking to potentially revisit defect prediction.
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van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.“I’m still / I’m still / Chaining from the Block”
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Cukic Promise08 V3
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