Software practitioners lack a consistent approach to assessing and predicting quality within their products. This research proposes a software quality model that accounts for the influences of development team skill/experience, process maturity, and problem complexity throughout the software engineering life cycle. The model is structured using Bayesian Belief Networks and, unlike previous efforts, uses widely-accepted software engineering standards and in-use industry techniques to quantify the indicators and measures of software quality. Data from 28 software engineering projects was acquired for this study, and was used for validation and comparison of the presented software quality models. Three Bayesian model structures are explored and the structure with the highest performance in terms of accuracy of fit and predictive validity is reported. In addition, the Bayesian Belief Networks are compared to both Least Squares Regression and Neural Networks in order to identify the technique is best suited to modeling software product quality. The results indicate that Bayesian Belief Networks outperform both Least Squares Regression and Neural Networks in terms of producing modeled software quality variables that fit the distribution of actual software quality values, and in accurately forecasting 25 different indicators of software quality. Between the Bayesian model structures, the simplest structure, which relates software quality variables to their correlated causal factors, was found to be the most effective in modeling software quality. In addition, the results reveal that the collective skill and experience of the development team, over process maturity or problem complexity, has the most significant impact on the quality of software products.

<p><em>Software Instrumentation is the process of collecting data during an application’s runtime,</em></p> <p><em>which will help us debug, detect errors and optimize the performance of the binary. The</em></p> <p><em>recent increase in demand for low latency and high throughput systems has introduced new</em></p> <p><em>challenges to the process of Software Instrumentation. Software Instrumentation, especially</em></p> <p><em>dynamic, has a huge impact on systems performance in scenarios where there is no early</em></p> <p><em>knowledge of data to be collected. Naive approaches collect too much or too little</em></p> <p><em>data, negatively impacting the system’s performance.</em></p> <p><em>This thesis investigates the overhead added by reservoir sampling algorithms at different</em></p> <p><em>levels of granularity in real-time instrumentation of distributed software systems. Also, this thesis describes the implementation of sampling techniques and algorithms to reduce the overhead caused by instrumentation.</em></p>

Software Quality is a key priority in any company involves with software development. Quality which can be describe as a distinguish feature of a software, has a high competitive advantage for most business organisations, especially during this turbulent time with the world battling pandemic. Software has grown and diversifies to ease our day to day life, therefore, the role of quality assurance activities has increase and become extremely important and complex. However, successful software which meets customer’s requirement and expectation depends on the quality of the software. In order to maintain the quality of their applications, several development industries have revised their quality procedure. DevOps and agile development have greatly improved the success rate of software projects with the introduction of test coverage measures. The purpose of this thesis is to implement existing test coverage metrics which are used for improving and measuring the quality of software in order to help reduce excess time consumption, overshooting of budget and maintain scope within requirement. The software quality metrics selected for this study is the ISO 9000 and the core focus area to implement this quality metrics is on unit test, integration test and acceptance test. For this requirement, a design workflow using a flowchart to get a clear description of the work process will be created, configuration of DevOps environment for the pipelines which combines continuous integration and continuous development (CI/CD) to test and build our code constantly and consistently with SonarCloud. Finally, configuration of TestProject for creating automated test script and automated acceptance test execution with automatic generation of test report as well as evaluating the quality of the software product based on the test execution and coverage result. The documentation for this implementation will contain all the steps necessary to configure the test coverage metrics. The metrics will be used to create unit tests, integration tests, and acceptance tests for web applications that run on a variety of browsers and versions, including Chrome Version 103.0.1264.37, Edge Version 103.0.5060.114, and Firefox Version 103.0.

Background. Machine learning (ML) has rapidly grown in popularity, becoming a vital part of many industries. This swift expansion has brought about new challenges to technical debt, maintainability and the general software quality of ML systems. With ML applications becoming more prevalent, there is an emerging need for extensive research to keep up with the pace of developments. Currently, the research on code smells in ML applications is limited and there is a lack of tools and studies that address these issues in-depth. This gap in the research highlights the necessity for a focused investigation into the validity of ML-specific code smells in ML applications, setting the stage for this research study. Objectives. Addressing the limited research on ML-specific code smells within Python-based ML applications. To achieve this, the study begins with the identification of these ML-specific code smells. Once recognized, the next objective is to choose suitable methods and tools to design and develop a static code analysis tool based on code smell criteria. After development, an empirical evaluation will assess both the tool’s efficacy and performance. Additionally, feedback from industry professionals will be sought to measure the tool’s feasibility and usefulness. Methods. This research employed Design Science Methodology. In the problem identification phase, a literature review was conducted to identify ML-specific code smells. In solution design, a secondary literature review and consultations with experts were performed to select methods and tools for implementing the tool. Additionally, 160 open-source ML applications were sourced from GitHub. The tool was empirically tested against these applications, with a focus on assessing its performance and efficacy. Furthermore, using the static validation method, feedback on the tool’s usefulness was gathered through an expert survey, which involved 15 ML professionals from Ericsson. Results. The study introduced MLpylint, a tool designed to identify 20 ML-specific code smells in Python-based ML applications. MLpylint effectively analyzed 160ML applications within 36 minutes, identifying in total 5380 code smells, although, highlighting the need for further refinements to each code smell checker to accurately identify specific patterns. In the expert survey, 15 ML professionals from Ericsson acknowledged the tool’s usefulness, user-friendliness and efficiency. However, they also indicated room for improvement in fine-tuning the tool to avoid ambiguous smells. Conclusions. Current studies on ML-specific code smells are limited, with few tools addressing them. The development and evaluation of MLpylint is a significant advancement in the ML software quality domain, enhancing reliability and reducing associated technical debt in ML applications. As the industry integrates such tools, it’s vital they evolve to detect code smells from new ML libraries. Aiding developers in upholding superior software quality but also promoting further research in the ML software quality domain.

Software security is an important aspect of ensuring software quality. The goal of this study is to help developers evaluate software security at the early stage of development using traceable patterns and software metrics. The concept of traceable patterns is similar to design patterns, but they can be automatically recognized and extracted from source code. If these patterns can better predict vulnerable code compared to the traditional software metrics, they can be used in developing a vulnerability prediction model to classify code as vulnerable or not. By analyzing and comparing the performance of traceable patterns with metrics, we propose a vulnerability prediction model. Objective: This study explores the performance of code patterns in vulnerability prediction and compares them with traditional software metrics. We have used the findings to build an effective vulnerability prediction model. Method: We designed and conducted experiments on the security vulnerabilities reported for Apache Tomcat (Releases 6, 7 and 8), Apache CXF and three stand-alone Java web applications of Stanford Securibench. We used machine learning and statistical techniques for predicting vulnerabilities of the systems using traceable patterns and metrics as features. Result: We found that patterns have a lower false negative rate and higher recall in detecting vulnerable code than the traditional software metrics. We also found a set of patterns and metrics that shows higher recall in vulnerability prediction. Conclusion: Based on the results of the experiments, we proposed a prediction model using patterns and metrics to better predict vulnerable code with higher recall rate. We evaluated the model for the systems under study. We also evaluated their performance in the cross-dataset validation.

Developing high quality software is the goal of every software development organization. Software quality models are commonly used to assess and improve the software quality. These models, based on the past releases of the system, can be used to identify the fault-prone modules for the next release. This information is useful to the open-source software community, including both developers and users. Developers can use this information to clean or rebuild the faulty modules thus enhancing the system. The users of the software system can make informed decisions about the quality of the product. This thesis builds quality models using logistic regression, neural networks, decision trees, and genetic algorithms and compares their performance. Our results show that an overall accuracy of 65 ? 85% is achieved with a type II misclassification rate of approximately 20 ? 35%. Performance of each of the methods is comparable to the others with minor variations.

Context: Software quality has a multi-faceted description encompassing several quality attributes. Central to our efforts to enhance software quality is to improve the quality of the source code. Poor source code quality impacts the quality of the delivered product. Empirical studies have investigated how to improve source code quality and how to quantify the source code improvement. However, the reported evidence linking internal code structure information and quality attributes observed by users is varied and, at times, conflicting. Furthermore, there is a further need for research to improve source code quality by understanding trends in feedback from code review comments. Objective: This thesis contributes towards improving source code quality and synthesizes metrics to measure improvement in source code quality. Hence, our objectives are 1) To synthesize evidence of links between source code metrics and external quality attributes, &amp; identify source code metrics, and 2) To identify areas to improve source code quality by identifying recurring code quality issues using the analysis of code review comments. Method: We conducted a tertiary study to achieve the first objective, an archival analysis and a case study to investigate the latter two objectives. Results: To quantify source code quality improvement, we reported a comprehensive catalog of source code metrics and a small set of source code metrics consistently linked with maintainability, reliability, and security. To improve source code quality using analysis of code review comments, our explored methodology improves the state-of-the-art with interesting results. Conclusions: The thesis provides a promising way to analyze themes in code review comments. Researchers can use the source code metrics provided to estimate these quality attributes reliably. In future work, we aim to derive a software improvement checklist based on the analysis of trends in code review comments.

Software refactoring is a crucial approach in both development and maintenance to improve the efficiency, maintainability, and structure of software systems. However, a number of challenges remain in the way of the effective implementation of software refactoring techniques within Sri Lanka's software development industry. This thesis investigates the challenger in software refactoring process in Sri Lanka software development companies and examine the intentions of developers, software test automation engineer and project managers on the usage on the machine learning techniques for software refactoring and the study uses the Unified Theory of Acceptance and usage of Technology 2 (UTAUT2) extended model. The study demonstrates that professional in software development Industry have positive intentions toward the usage of machine learning techniques, motivated by benefits they perceive, such as increased productivity, maintenance, and improved code quality. This study advances our understanding of software refactoring and theadoption of new ML technologies and offers insightful information to researchers, practitioners, and decision- makers in the Sri Lankan IT sector and beyond.

The focus of the dissertation is on software quality assurance for object-oriented information systems development. A Quality Assurance Reference Model is proposed with aspects dealing with technical and managerial issues. A revised Spiral life cycle model is adopted as well as the Object Modelling Technique. The Quality Assurance Reference Model associates quality factors at various levels, quality criteria and metrics into a matrix framework that may be used to achieve quality assurance for all cycles of the Spiral Model. / Computing / M. Sc. (Information Systems)

The focus of the dissertation is on software quality assurance for object-oriented information systems development. A Quality Assurance Reference Model is proposed with aspects dealing with technical and managerial issues. A revised Spiral life cycle model is adopted as well as the Object Modelling Technique. The Quality Assurance Reference Model associates quality factors at various levels, quality criteria and metrics into a matrix framework that may be used to achieve quality assurance for all cycles of the Spiral Model. / Computing / M. Sc. (Information Systems)