Development of a Maturity Model for Software Quality Assurance Practices

Aims: Software quality assurance is a formal process for evaluating and documenting the quality of the work products during each stage of the software development lifecycle.The practice of applying software metrics to operational factors and to maintain factors is a complex task.Successful software quality assurance is highly dependent on software metrics.It needs linkage the software quality model and software metrics through quality factors in order to offer measure method for software quality assurance.The contributions of this paper build an appropriate method of Software quality metrics application in quality life cycle with software quality assurance.Design: The purpose approach defines some software metrics in the factors and discussed several software quality assurance model and some quality factors measure method.Methodology: This paper solves customer value evaluation problem are: Build a framework of combination of software quality criteria.Describes software metrics.Build Software quality metrics application in quality life cycle with software quality assurance.Results: From the appropriate method of Software quality metrics application in quality life cycle with software quality assurance, each activity in the software life cycle, there is one or more QA quality measure metrics focus on ensuring the quality of the process and the resulting product.Future research is need to extend and improve the methodology to extend metrics that have been validated on one project, using our criteria, valid measures of quality on future software project.

The U.S. Geological Survey's automated information system's software for processing, storing, and retrieving water data is developed and maintained by the National Water Information System Program of the Water Resources Division. As part of that responsibility, a new system is currently being designed to replace the systems that run on a mainframe computer located at headquarters in Reston, Vz., and on minicomputers located in Division offices across the Nation. The new system is to run on workstations and file servers to be installed in all of the Division offices. A software life cycle provides the framework for defining, developing, and maintaining the new software system and for helping to meet software development challenges. Soft*'are quality assurance is needed in building the new system to provide confidence that the software is engineered to meet users' needs and conforms to established technical requirements. Successful software quality assurance depends on the application of uniform terminology and effective communication among users, developers, and managers. Software quality control is a planned and systematic process that focuses on establishing software standards and procedures, reviews and inspections, testing, and software configuration management throughout the software life cycle. This report explains the background for software quality assurance and the implementation of software quality assurance and quality control in the National Water Information System Program using a generic workbench approach, and presents an example workbench for the requirements-analysis phase of the software life-cycle.

Putting Requirements and Quality at the Core of Global Service Delivery: Current Efforts and Future Plans at Pace University

Software quality assurance (SQA) must be able to support the practical application. This chapter presents the models as well as the software engineering standards available for correctly defining performance or non‐functional requirements of software. It describes the research carried out over the years that culminated in a definition of a software quality model. Quality assurance, in certain industries and business models overseen by the software developer, requires a more formal management of software quality throughout the software life cycle. Quality is often measured by considering a specific perception of quality. The chapter provides the SQA practitioner with the knowledge to use the ISO 25010 software quality model concepts. It presents the history of the different models and standards designed to characterize software quality. The chapter introduces the concept of quality requirements and presents a process for defining quality requirements.

Constant changes in a business context and software development make it important to understand how software quality assurance (SQA) should respond. Examining SQA from supplier and client perspectives, this study explores how different groups of SQA practitioners perceive future needs. A survey (n = 93) conducted in fall 2017 explored the views of SQA organizations on future trends. The results indicate that SQA organizations differ slightly in their attitudes to quality categories, as do different groups of SQA practitioners. It is argued that these differences should be taken into account when developing and implementing future SQA strategy. It is further argued that the found basic enables SQA management, evaluation of new practices, and allocation of resources to ensure that all quality categories remain balanced in the future.

The support of software quality in a software development process may be regarded under two aspects: first, by providing techniques, which support the development of high quality software and second, by providing techniques, which assure the required quality attributes in existing artifacts. Both approaches have to be combined to achieve effective and successful software engineering.In this study, we compare three of the most industrially relevant software development process models (Rational Unified Process (RUP), Microsoft Solution Framework (MSF) and Extreme Programming (XP)) regarding their software quality support in terms of software quality development and software quality assurance. Based on the results we propose a de-facto standard for quality support in software development process models.

One of the most serious concerns of modern software engineering is software quality assurance, whereas the traditional methods of testing and fault detection are commonly time-consuming, resource-consuming, and cannot always reveal the defects promptly. The recent developments in machine learning (ML) offer a possibility to improve the software development life cycle (SDLC) with predictive and automated Fault detection models. The study examines the formation and implementation of ML-powered fault prediction systems in SDLC with the goal of finding defective code modules during the design, code and version control phases. ML models can also be trained using software metrics and past Fault data, and logs of developer activity to predict the fault-prone components with a great degree of accuracy. This is not merely the proposed model reduces the effort of testing and cost of maintenance; however, the overall reliability of the software is also increased as the attention of high-risk modules is concentrated on review. Issues such as data imbalance, the explainability of the models, and cross-project generalization are also taken into account and present a feasible interpretation of the way that ML can be introduced in software quality assurance in practice. The possibility of automating software engineering by using ML will be indicated in the current paper to make it more proactive, intelligent, and efficient.

The moderator has offered seven propositions relative to the meaning and conduct of effective software quality assurance. Although I find it difficult to give unqualified acceptance or rejection to any of these propositions, in response to the rules of the session I accept propositions 1, 2, 3 and 5 and reject 4, 6 and 7.After a number of years experience in the software quality arena, both in government and industry, I have come to the conclusion that the most important aspect of software quality assurance is to make a positive contribution to the objective of the software project. I define this objective as the development of a quality software product delivered on time and within budget. I further define a quality software product as one which meets customer requirements and provides usable service. In short my perspective of software quality assurance is one of contribution in contrast to the “righteousness of the cause.”To this end I suggest an “engineering analysis approach” to software quality. Such an approach can take the form of a typical engineering problem solving model with steps of problem formulation, problem analysis, search, decision, specification and implementation. Using such an approach the advocates for quality software can use empirical evidence to establish the contributions software quality assurance efforts make toward the software project objective.

The concept of software quality assurance (SQA) is rapidly evolving, and reflects a growing awareness of the critical importance of this field. However, our view of what SQA is all about needs to focus more on the interrelated aspects of integration and scope . In terms of integration, SQA must become an integral part of every task in the development process, rather than being a parallel function embodied in a separate organizational department. With respect to scope, SQA must encompass areas that are often seen as marginal to software quality. In particular, the areas of business policy, metrics, peopleware, technology transfer, and technical leadership must be addressed.

Background & Problem Statement - Software testing is a critical phase in the software development lifecycle (SDLC), ensuring that applications function correctly, meet user requirements, and maintain high- quality standards. Traditional software testing approaches, including manual testing and rule-based automation, often face challenges in scalability, efficiency, and adaptability to dynamic software environments. Traditional testing methods are overwhelmed by complex software systems which slows down defect detection and extends both testing costs and release schedules. Machine Learning (ML) has emerged as a transformative solution, introducing predictive and adaptive capabilities that optimize test case selection, automate defect detection, and enhance overall software quality assurance (QA). This study explores the integration of ML in software testing, addressing the challenges of traditional QA methodologies and demonstrating how AI-driven frameworks improve testing efficiency. Methodology - To investigate the impact of ML in software testing, this research adopts a systematic approach by analyzing ML-driven test automation techniques, including predictive testing, adaptive test execution, and automated test case generation. Research reviews how Google Microsoft Facebook IBM and Deep Code put ML-based quality assurance frameworks into operation. The study leverages supervised learning, reinforcement learning, deep learning, and NLP-based techniques to demonstrate how ML models predict software defects, dynamically adapt test cases, and optimize testing resources. The research tests how ML-based testing models operate within CI/CD pipelines to improve ongoing testing and deployment flow. Analysis & Results - The analysis of ML-driven software testing reveals that predictive analytics improves early defect detection rates. It helps developers spend 37% less time debugging their work. Adaptive testing models, including self-healing test scripts, minimize maintenance costs by 50% and enhance test reliability in agile environments. The integration of NLP-based test case generation increases test coverage. NLP technology enables automatic connection between requirements and test cases at 89% success rate. Additionally, reinforcement learning techniques improve test case selection, reducing redundant test executions by 43%. Our research shows different ML methods work well to lessen incorrect error alerts. ML integration for QA surely increasing defect prediction accuracy and optimizing test execution time. Findings & Contributions - This research contributes to the field of AI-driven software testing by providing a comprehensive framework for ML-based QA methodologies. Our study shows that machine learning helps find more software problems better adapts test cases and lowers testing expenses to solve present software development needs. The study also identifies critical challenges, including data availability, model interpretability, and computational overhead, suggesting future research directions in Explainable AI (XAI), hybrid AI-ML testing models, and AI-driven security testing. As the industry moves toward AI-first software testing, this research paves the way for fully autonomous QA frameworks, enabling intelligent, scalable, and cost- effective software validation techniques. Keywords - Machine Learning, Software Testing, Quality Assurance, Predictive Testing, Adaptive Testing, Test Automation, Defect Prediction, Self- Healing Test Scripts, AI-Driven QA, Reinforcement

Software Quality Assurance (SQA) activities are exercised to ensure high-quality software systems. Defect models help developers identify the most risky modules to prioritise their limited SQA resources. The interpretation of defect models also helps managers understand what factors impact software quality to chart quality improvement plans. Unfortunately, the commonly-used interpretation techniques (e.g., ANOVA for logistic regression and variable importance for random forests) only explain defect models at the high level (e.g., what factors impact software quality). Researchers and practitioners also raise concerns about a lack of explainability of defect models that hinders the adoption in practice. This thesis hypothesises that: A lack of explainability poses a critical challenge when adopting defect models in practice. To validate the hypothesis, we formulate 3 research questions, i.e., (1) what is the best defect modelling workflow that produces the most accurate and reliable interpretation of defect models?, (2) what is the best technique for explaining the predictions of defect models?, and (3) how do practitioners perceive when adopting explainable defect models? Through case studies of publicly-available open-source and industrial software systems, the results show that correlated variables impact the interpretation of defect models and must be mitigated; our proposed feature selection technique, AutoSpearman, is the only studied feature selection technique that can automatically mitigate correlated variables with a little impact on model performance; and the instance-level interpretation of defect models is needed to derive actionable insights to guide operational and technical decisions in SQA efforts.

The paper presents the findings of a survey on aspects of software quality assurance (SQA). Fifty-two sets of questionnaires were collected for analysis. Brief discussions on the survey methodology, data processing and analysis are included. Highlights of this survey encompass the trends of SQA investments, QA problems encountered, the tools being used and their weaknesses, as well as the people who are involved in SQA activities.

Many problems, related with safety, frequently occur because Digital Instrument & Control Systems are widely used and expanding their ranges to many applications in Nuclear Power Plants. It, however, does not hold a general position to estimate an appropriate software quality. Thus, the Quality Characteristic Value, a software quality factor through each software life cycle, is suggested in this paper. The Quality Characteristic Value is obtained as following procedure: 1) Scoring Quality Characteristic Factors (especially correctness, traceability, completeness, and understandability) onto Software Verification and Validation results, 2) Deriving the diamond-shaped graphs by setting values of Factors at each axis and lining every points, and lastly 3) Measuring the area of the graph for Quality Characteristic Value. In this paper, this methodology is applied to Plant Control System. In addition, the series of quantification frameworks exhibit some good characteristics in the view of software quality factor. More than any thing else, it is believed that introduced framework may be applicable to regulatory guide, software approval procedures, due to its soundness and simple characteristics.

Terms related to software quality are described, including software quality, software assurance, software quality assurance, validation, and verification. Three software quality assurance (QA) methods and four software testing approaches are described. The three QA methods are formal review, informal review, and code/design walkthrough. The four testing approaches are unit, integration, systems, and acceptance.

This paper proposes an examination of effective methods and tools for ensuring software quality. The scope of this topic includes current issues related to software quality assurance within the context of analyzing methods and tools used in practice to develop high-quality software. During the modeling process, a new comprehensive model for software quality assurance has been developed, combining modular testing, integration testing, and continuous integration methods. The advantage of this development is its enhanced adaptability to addressing key challenges in software quality assurance. Based on the developed model, strategies and approaches are proposed to improve configuration management processes and identify vulnerabilities in software systems.