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Collaborative quality assurance in information systems development : the interaction of software development techniques and team cognition
This book examines how and why collaborative quality assurance techniques, particularly pair programming and peer code review, affect group cognition and software quality in agile software development teams. Prior research on these extremely popular but also costly techniques has focused on isolated pairs of developers and ignored the fact that they are typically applied in larger, enduring teams. This book is one of the first studies to investigate how these techniques depend on and influence the joint cognitive accomplishments of entire development teams rather than individuals. It employs theories on transactive memory systems and functional affordances to provide answers based on empirical research. The mixed-methods research presented includes several in-depth case studies and survey results from more than 500 software developers, team leaders, and product managers in 81 software development teams. The book's findings will advance IS research and have explicit implications for developers of code review tools, information systems development teams, and software development managers.
Book
An explainable AI framework for enhanced software defect prediction using transformer-assisted boosting
Accurate defect prediction is essential for better software quality to avoid cost overruns, schedule delays, and reduced system reliability due to software defects. This study presents a Transformer Assisted Boosting Framework (TABF) that combines XGBoost with the Transformer’s self-attention to achieve higher predictive accuracy and interpretability. The framework is evaluated using the NASA Metrics Data Program (MDP) and the Code4Code dataset, which comprises software metrics such as cyclomatic complexity, Halstead’s properties, and lines of code. Experimental results demonstrate that the performance of TABF, with AUC scores of 0.95 and ROC of 0.96, is superior to classical machine learning models, such as Random Forest and SVM, with accuracies of 92.5% and 94.3%, respectively. SHapley Additive exPlanations (SHAP) are used to explain feature importance, uncovering that lines of code and McCabe’s cyclomatic complexity are among the most important predictors of software defects. These insights are used for defect management and resource allocation, as well as to improve software reliability. TABF unifies high-performance predictive modeling with explainability and closes the gap between machine learning or deep learning-based defect prediction models and their use by software quality assurance practitioners in practice.
Journal Article
Enhancing Software Comments Readability Using Flesch Reading Ease Score
Comments are used to explain the meaning of code and ease communications between programmers themselves, quality assurance auditors, and code reviewers. A tool has been developed to help programmers write readable comments and measure their readability level. It is used to enhance software readability by providing alternatives to both keywords and comment statements from a local database and an online dictionary. It is also a word-finding query engine for developers. Readability level is measured using three different formulas: the fog index, the Flesch reading ease score, and Flesch–Kincaid grade levels. A questionnaire has been distributed to 42 programmers and 35 students to compare the readability aspect between both new comments written by the tool and the original comments written by previous programmers and developers. Programmers stated that the comments from the proposed tool had fewer complex words and took less time to read and understand. Nevertheless, this did not significantly affect the understandability of the text, as programmers normally have quite a high level of English. However, the results from students show that the tool affects the understandability of text and the time taken to read it, while text complexity results show that the tool makes new comment text that is more readable by changing the three studied variables.
Journal Article
Supporting the identification of prevalent quality issues in code changes by analyzing reviewers’ feedback
Context: Code reviewers provide valuable feedback during the code review. Identifying common issues described in the reviewers’ feedback can provide input for devising context-specific software development improvements. However, the use of reviewer feedback for this purpose is currently less explored. Objective: In this study, we assess how automation can derive more interpretable and informative themes in reviewers’ feedback and whether these themes help to identify recurring quality-related issues in code changes. Method: We conducted a participatory case study using the JabRef system to analyze reviewers’ feedback on merged and abandoned code changes. We used two promising topic modeling methods (GSDMM and BERTopic) to identify themes in 5,560 code review comments. The resulting themes were analyzed and named by a domain expert from JabRef. Results: The domain expert considered the identified themes from the two topic models to represent quality-related issues. Different quality issues are pointed out in code reviews for merged and abandoned code changes. While BERTopic provides higher objective coherence, the domain expert considered themes from short-text topic modeling more informative and easy to interpret than BERTopic-based topic modeling. Conclusions: The identified prevalent code quality issues aim to address the maintainability-focused issues. The analysis of code review comments can enhance the current practices for JabRef by improving the guidelines for new developers and focusing discussions in the developer forums. The topic model choice impacts the interpretability of the generated themes, and a higher coherence (based on objective measures) of generated topics did not lead to improved interpretability by a domain expert.
Journal Article
Jenkins 2 : up and running : evolve your deployment pipeline for next-generation automation
Design, implement, and execute continuous delivery pipelines with a level of flexibility, control, and ease of maintenance that was not possible with Jenkins before. With this practical book, build administrators, developers, testers, and other professionals will learn how the features in Jenkins 2 let you define pipelines as code, leverage integration with other key technologies, and create automated, reliable pipelines to simplify and accelerate your DevOps environments.
Book
Software Coupling and Cohesion Model for Measuring the Quality of Software Components
Measuring software quality requires software engineers to understand the system’s quality attributes and their measurements. The quality attribute is a qualitative property; however, the quantitative feature is needed for software measurement, which is not considered during the development of most software systems. Many research studies have investigated different approaches for measuring software quality, but with no practical approaches to quantify and measure quality attributes. This paper proposes a software quality measurement model, based on a software interconnection model, to measure the quality of software components and the overall quality of the software system. Unlike most of the existing approaches, the proposed approach can be applied at the early stages of software development, to different architectural design models, and at different levels of system decomposition. This article introduces a software measurement model that uses a heuristic normalization of the software’s internal quality attributes, i.e., coupling and cohesion, for software quality measurement. In this model, the quality of a software component is measured based on its internal strength and the coupling it exhibits with other component(s). The proposed model has been experimented with nine software engineering teams that have agreed to participate in the experiment during the development of their different software systems. The experiments have shown that coupling reduces the internal strength of the coupled components by the amount of coupling they exhibit, which degrades their quality and the overall quality of the software system. The introduced model can help in understanding the quality of software design. In addition, it identifies the locations in software design that exhibit unnecessary couplings that degrade the quality of the software systems, which can be eliminated.
Journal Article
ML-Based Software Defect Prediction in Embedded Software for Telecommunication Systems (Focusing on the Case of SAMSUNG ELECTRONICS)
Software stands out as one of the most rapidly evolving technologies in the present era, characterized by its swift expansion in both scale and complexity, which leads to challenges in quality assurance. Software defect prediction (SDP) has emerged as a methodology crafted to anticipate undiscovered defects, leveraging known defect data from existing codes. This methodology serves to facilitate software quality management, thereby ensuring overall product quality. The methodologies of machine learning (ML) and one of its branches, deep learning (DL), exhibit superior accuracy and adaptability compared to traditional statistical approaches, catalyzing active research in this domain. However, it makes it hard to generalize, not only because of the disparity between open-source projects and commercial projects but also due to the differences in each industrial sector. Consequently, further research utilizing datasets sourced from diverse real-world sectors has become imperative to bolster the applicability of these findings. For this study, we utilized embedded software for use with the telecommunication systems of Samsung Electronics, supplemented by the introduction of nine novel features to train the model, and a subsequent analysis of the results ensued. The experimental outcomes revealed that the F-measurement metric has been enhanced from 0.58 to 0.63 upon integration of the new features, thereby signifying a performance augmentation of 8.62%. This case study is anticipated to contribute to bolstering the application of SDP methodologies within analogous industrial sectors.
Journal Article