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"Computer software Development Quality control."
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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
A family of experiments on test-driven development
Context:Test-driven development (TDD) is an agile software development approach that has been widely claimed to improve software quality. However, the extent to which TDD improves quality appears to be largely dependent upon the characteristics of the study in which it is evaluated (e.g., the research method, participant type, programming environment, etc.). The particularities of each study make the aggregation of results untenable.Objectives:The goal of this paper is to: increase the accuracy and generalizability of the results achieved in isolated experiments on TDD, provide joint conclusions on the performance of TDD across different industrial and academic settings, and assess the extent to which the characteristics of the experiments affect the quality-related performance of TDD.Method:We conduct a family of 12 experiments on TDD in academia and industry. We aggregate their results by means of meta-analysis. We perform exploratory analyses to identify variables impacting the quality-related performance of TDD.Results:TDD novices achieve a slightly higher code quality with iterative test-last development (i.e., ITL, the reverse approach of TDD) than with TDD. The task being developed largely determines quality. The programming environment, the order in which TDD and ITL are applied, or the learning effects from one development approach to another do not appear to affect quality. The quality-related performance of professionals using TDD drops more than for students. We hypothesize that this may be due to their being more resistant to change and potentially less motivated than students.Conclusion:Previous studies seem to provide conflicting results on TDD performance (i.e., positive vs. negative, respectively). We hypothesize that these conflicting results may be due to different study durations, experiment participants being unfamiliar with the TDD process, or case studies comparing the performance achieved by TDD vs. the control approach (e.g., the waterfall model), each applied to develop a different system. Further experiments with TDD experts are needed to validate these hypotheses.
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
Empirical research on requirements quality: a systematic mapping study
Research has repeatedly shown that high-quality requirements are essential for the success of development projects. While the term “quality” is pervasive in the field of requirements engineering and while the body of research on requirements quality is large, there is no meta-study of the field that overviews and compares the concrete quality attributes addressed by the community. To fill this knowledge gap, we conducted a systematic mapping study of the scientific literature. We retrieved 6905 articles from six academic databases, which we filtered down to 105 relevant primary studies. The primary studies use empirical research to explicitly define, improve, or evaluate requirements quality. We found that empirical research on requirements quality focuses on improvement techniques, with very few primary studies addressing evidence-based definitions and evaluations of quality attributes. Among the 12 quality attributes identified, the most prominent in the field are ambiguity, completeness, consistency, and correctness. We identified 111 sub-types of quality attributes such as “template conformance” for consistency or “passive voice” for ambiguity. Ambiguity has the largest share of these sub-types. The artefacts being studied are mostly referred to in the broadest sense as “requirements”, while little research targets quality attributes in specific types of requirements such as use cases or user stories. Our findings highlight the need to conduct more empirically grounded research defining requirements quality, using more varied research methods, and addressing a more diverse set of requirements types.
Journal Article
Requirements quality research: a harmonized theory, evaluation, and roadmap
High-quality requirements minimize the risk of propagating defects to later stages of the software development life cycle. Achieving a sufficient level of quality is a major goal of requirements engineering. This requires a clear definition and understanding of requirements quality. Though recent publications make an effort at disentangling the complex concept of quality, the requirements quality research community lacks identity and clear structure which guides advances and puts new findings into an holistic perspective. In this research commentary, we contribute (1) a harmonized requirements quality theory organizing its core concepts, (2) an evaluation of the current state of requirements quality research, and (3) a research roadmap to guide advancements in the field. We show that requirements quality research focuses on normative rules and mostly fails to connect requirements quality to its impact on subsequent software development activities, impeding the relevance of the research. Adherence to the proposed requirements quality theory and following the outlined roadmap will be a step toward amending this gap.
Journal Article
An empirical study of some software fault prediction techniques for the number of faults prediction
During the software development process, prediction of the number of faults in software modules can be more helpful instead of predicting the modules being faulty or non-faulty. Such an approach may help in more focused software testing process and may enhance the reliability of the software system. Most of the earlier works on software fault prediction have used classification techniques for classifying software modules into faulty or non-faulty categories. The techniques such as Poisson regression, negative binomial regression, genetic programming, decision tree regression, and multilayer perceptron can be used for the prediction of the number of faults. In this paper, we present an experimental study to evaluate and compare the capability of six fault prediction techniques such as genetic programming, multilayer perceptron, linear regression, decision tree regression, zero-inflated Poisson regression, and negative binomial regression for the prediction of number of faults. The experimental investigation is carried out for eighteen software project datasets collected from the PROMISE data repository. The results of the investigation are evaluated using average absolute error, average relative error, measure of completeness, and prediction at
level l
measures. We also perform Kruskal–Wallis test and Dunn’s multiple comparison test to compare the relative performance of the considered fault prediction techniques.
Journal Article
OpenCASA: A new open-source and scalable tool for sperm quality analysis
In the field of assisted reproductive techniques (ART), computer-assisted sperm analysis (CASA) systems have proved their utility and potential for assessing sperm quality, improving the prediction of the fertility potential of a seminal dose. Although most laboratories and scientific centers use commercial systems, in the recent years certain free and open-source alternatives have emerged that can reduce the costs that research groups have to face. However, these open-source alternatives cannot analyze sperm kinetic responses to different stimuli, such as chemotaxis, thermotaxis or rheotaxis. In addition, the programs released to date have not usually been designed to encourage the scalability and the continuity of software development. We have developed an open-source CASA software, called OpenCASA, which allows users to study three classical sperm quality parameters: motility, morphometry and membrane integrity (viability) and offers the possibility of analyzing the guided movement response of spermatozoa to different stimuli (useful for chemotaxis, thermotaxis or rheotaxis studies) or different motile cells such as bacteria, using a single software. This software has been released in a Version Control System at Github. This platform will allow researchers not only to download the software but also to be involved in and contribute to further developments. Additionally, a Google group has been created to allow the research community to interact and discuss OpenCASA. For validation of the OpenCASA software, we analysed different simulated sperm populations (for chemotaxis module) and evaluated 36 ejaculates obtained from 12 fertile rams using other sperm analysis systems (for motility, membrane integrity and morphology modules). The results were compared with those obtained by Open-CASA using the Pearson's correlation and Bland-Altman tests, obtaining a high level of correlation in all parameters and a good agreement between the different used methods and the OpenCASA. With this work, we propose an open-source project oriented to the development of a new software application for sperm quality analysis. This proposed software will use a minimally centralized infrastructure to allow the continued development of its modules by the research community.
Journal Article
Prioritizing tasks in software development: A systematic literature review
Task prioritization is one of the most researched areas in software development. Given the huge number of papers written on the topic, it might be challenging for IT practitioners–software developers, and IT project managers–to find the most appropriate tools or methods developed to date to deal with this important issue. The main goal of this work is therefore to review the current state of research and practice on task prioritization in the Software Engineering domain and to individuate the most effective ranking tools and techniques used in the industry. For this purpose, we conducted a systematic literature review guided and inspired by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, otherwise known as the PRISMA statement. Based on our analysis, we can make a number of important observations for the field. Firstly, we found that most of the task prioritization approaches developed to date involve a specific type of prioritization strategy— bug prioritization . Secondly, the most recent works we review investigate task prioritization in terms of “pull request prioritization” and “issue prioritization,” (and we speculate that the number of such works will significantly increase due to the explosion of version control and issue management software systems). Thirdly, we remark that the most frequently used metrics for measuring the quality of a prioritization model are f-score , precision , recall , and accuracy .
Journal Article
Integrating temporal convolutional networks with metaheuristic optimization for accurate software defect prediction
The increasing importance of deep learning in software development has greatly improved software quality by enabling the efficient identification of defects, a persistent challenge throughout the software development lifecycle. This study seeks to determine the most effective model for detecting defects in software projects. It introduces an intelligent approach that combines Temporal Convolutional Networks (TCN) with Antlion Optimization (ALO). TCN is employed for defect detection, while ALO optimizes the network’s weights. Two models are proposed to address the research problem: (a) a basic TCN without parameter optimization and (b) a hybrid model integrating TCN with ALO. The findings demonstrate that the hybrid model significantly outperforms the basic TCN in multiple performance metrics, including area under the curve, sensitivity, specificity, accuracy, and error rate. Moreover, the hybrid model surpasses state-of-the-art methods, such as Convolutional Neural Networks, Gated Recurrent Units, and Bidirectional Long Short-Term Memory, with accuracy improvements of 21.8%, 19.6%, and 31.3%, respectively. Additionally, the proposed model achieves a 13.6% higher area under the curve across all datasets compared to the Deep Forest method. These results confirm the effectiveness of the proposed hybrid model in accurately detecting defects across diverse software projects.
Journal Article