Applying Testing to Enhance Software Product Quality

: With regard to software testing in DoD, we can summarize our conclusions in two fundamental ideas. First, DoD knows how to produce quality software at low cost. This is because organizations such as DoD STEP, Army STEP, and Software Engineering Institute have already researched and documented policies for DoD. A few commercial software developers practice many of the DoD policies and directives now, and produce quality software (for example, IBM FSC Houston). Second, quality cannot be tested into software. Only a well-defined, well-disciplined process with a continuous improvement cycle can ensure software quality. However, testing cannot be underestimated. Systematic testing activities that detect error earliest in the life cycle are necessary to drive process improvement and optimize the development of quality software. Such testing methods as formal inspection find defects early. This enables cost- effective error resolution, identification and removal of defect causes, and thus, prevention of future defect insertion. If practiced with discipline, such methods can evolve a self-correcting software development process that is stable, modeled, measured, and therefore, predictable. This development process engineers quality software faster at reduced cost.... Software testing, Software quality, Formal inspection, Quality control.

Today, the most vital step is software testing, especially in product quality. Agile software development is exceedingly difficult and has contentious issues in literature and business. Since most Agile techniques place more emphasis on development than software testing activities, different people have varying opinions about how software testing fits into these methodologies. Agile places a high emphasis on frequent deliveries and tight client connections. However, software testing is complex because agile does not incorporate many destructive testing techniques typically required for a product of higher-quality. The study reveals industry-wide Agile testing procedures and the main problems that arise when Agile development is used. Additionally covered are the differences between manual and automated testing, best automation practices, and ways to lead different testing teams in an agile testing environment. This study covers every facet of the Agile software testing process. Agile development has shown that testing is not a distinct stage but rather a crucial component of software development along with the coding. The agile team employs an overall strategy to produce software products of the highest quality. As part of an agile team, testers collect customer samples of the desired behaviour and collaborate with the developer's team to help the coders translate these examples into executable specifications. Coding and testing are done iteratively and incrementally, developing every item until it is valuable enough to release in production. It addresses several testing modalities. The classification provided by its quadrants aids teams in determining and organizing the necessary tests. To increase quality assurance, this article will examine various and improved Agile Testing Processes and methodologies. Additionally, the topic of Agile development's software testing method is covered. Agile development processes can be improved and more advantageous by incorporating testing procedures.

Software testing is an important process in software quality assurance(SQA). Software testing consumes the majority of the software developers' time and cost of all the phases of system development. An important problem in software testing is to determine whether a program has been tested enough with a testing criterion and when one could stop testing. On the other hand, software measurement is used to estimate the quality of a software. Complexity measures can be used to compare software quality and development cost. This paper proposes a new testing approach which choosing testing path based on path complexity. The process is to use a set of testing paths that satisfying all-uses testing criterion, and then choose the testing path with the most complexity to test. This process is continued until the program can be accepted. The proposed formula is used to determine if it is possible to stop testing before satisfying all-du-paths testing criterion. The main contribution of the paper is that software developers can choose a proper testing action with lower testing effort.

Multi-attribute decision making (MADM) is a state-of-the-art, popular technique for dealing with real-world problems. An effective decision can be made for various real-world problems involving multiple attributes to decide a proper solution. Software testing and bug fixing are essential steps in the field of software engineering. Bug triaging is a real challenge in large-scale software development. Bug triaging is the process of allocating newly reported bugs to the best developer who meets the requirements for addressing them. In this paper, a software developer ranking model based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is proposed for an effective bug triaging process. Developers are ranked based on various criteria using the TOPSIS model for effective bug triaging. Assigning newly reported bugs to the appropriate and available software developer is a complex decision-making process. It involves consideration of multiple criteria for discovering the optimal solution. In software engineering, bug triaging refers to the process of allocating appropriate developers to a newly reported bug. This paper presents criteria for finding suitable developers using multi-criteria decision-making (MCDM) techniques. The Analytic Hierarchy Process (AHP) method is used to determine the weights of the criteria, and the TOPSIS MCDM technique is used to rank the most appropriate developer.

Software Testing from an Agile and Traditional view

<div class="section abstract"><div class="htmlview paragraph">The development and the test of complex electronic control units for power train control systems (with focus on DCT transmission control) requires a well established, functional and feasible development process and modern development tools to fulfil all target requirements including cost and project time requirements. This development tasks are no longer imaginable without the usage of simulation tools. Whereat simulation has been used for Hardware-In-Loop (HIL) testing and the software has been and hand-written for years, simulation tools become more important in all areas of development. In fields like system development, software development, software testing and software calibration, simulation tools are used to improve the execution of development tasks.</div><div class="htmlview paragraph">This paper describes the implementation of a tool-driven and model-based software development process with focus on the model-based software design and testing possibilities as well as the usage of profound simulation capabilities and the preparation of the development process for the development of functional safety according ISO 26262. Based on examples from existing development projects for double clutch transmission control systems, the implementation of the development process and the usage of software tools will be shown.</div></div>

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

This paper proposes the design of specific programme dedicated for students who want to specialize in software testing at postgraduate level. The motivation behind this proposal is to encourage more higher learning institutions to offer variety of software engineering related programme, in particular software testing so that this area is recognized as another key contributor for developing competent testing professionals. Although there are many software testing professional certifications available in the market, there is still a need for offering this kind of programme in university at postgraduate level to nurture the importance of software testing in producing quality software. The proposed programme is designed based on coursework basis to mainly cater for the working students. As the structure emphasizes on mix of theories and practical knowledge with the use of suitable testing tools, thus the substance of this programme should at least cover the fundamentals knowledge of testing, every key types of testing performed today such as functional testing, security testing, performance testing, usability testing and compatibility testing as well as latest trends in testing such as testing in agile environment. It is expected that the proposed programme should be able to produce graduates that demonstrate advanced competencies in software testing and able to apply the acquired skills in producing high quality software.

Software Testing is an important aspect of the real time software development process. Software testing always assures the quality of software product. As associated with software testing, there are few very important issues where there is a need to pay attention on it in the process of software development test. These issues are generation of effective test case and test suite as well as optimization of test case and suite while doing testing of software product. The important issue is that testing time of the test case and test suite. It is very much important that after development of software product effective testing should be performed. So to overcome these issues of optimization, we have proposed new approach for test suite optimization using genetic algorithm (GA). Genetic algorithm is evolutionary in nature so it is often used for optimization of problem by researcher. In this paper, our aim is to study various selections methods like tournament selection, rank selection and roulette wheel selection and then we apply this genetic algorithm (GA) on various programs which will generate optimized test suite with parameters like fitness value of test case, test suite and take minimum amount of time for execution after certain preset generation. In this paper our main objectives as per the experimental investigation, we show that tournament selection works very fine as compared to other methods with respect fitness selection of test case and test suites, testing time of test case and test suites as well as number of requirements.

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

Software quality assurance and testing is a vital phase in the software development lifecycle, oftentimes requiring a significant portion of the overall development budget. Providing authentic, real-world software quality assurance and testing experiences within the context of a Computer Science or Software Engineering curriculum is challenging. Based on prior experience leveraging Free and Open Source Software (FOSS) as an instructional medium for teaching Software Engineering concepts, this paper describes five increasingly involved learning interventions that utilize FOSS in a software testing course to provide students real-world hands-on software testing knowledge and experience. The proposed approach will be piloted and assessed in software testing courses at the undergraduate and graduate level.

The low-code approach is an important area of research being developed to improve the rapid creation and performance of software applications. This approach allows developers and users to easily create software applications through an interactive interface. Existing research shows that the low-code approach accelerates the development process in innovative ways, saves effort and time, and reduces code complexity. This study analyses and compares automatic code generation and transformation techniques in low-code platforms. The study analyses the contributions of automatic code generation and transformation to software engineering processes and evaluates the impact of these techniques on software product quality and development speed. The importance of the Model-Based Development (MBD) approach in automated code generation processes is highlighted. It is stated that MBD provides benefits such as speeding up the software development process and reducing errors by enabling automatic code generation from high-level abstract models. In the study, various literature studies were evaluated to examine the impact of automatic code generation and transformation techniques on the application development process. These studies have shown how automatic code generation and transformation techniques are applied, what results are achieved and what contributions these techniques make to the software development process. The results of the study show that automatic code generation and transformation techniques have a great impact on the application development process. In particular, the acceleration of the software development process, the reduction of errors and the ability to produce more effective and efficient software products are important benefits of these techniques. In addition, it has been found that this approach increases the success of software projects by enabling software developers to produce fast and accurate solutions in complex systems. In this context, the importance of automatic code generation and transformation techniques plays an important role in software development processes.

Software testing is a critical and time-consuming element of the software development life cycle (SDLC). Software testing determines the quality of a software before it is used by end users. The main goal is to find software errors or defects so that these errors or defects can be corrected in this testing phase. There are two general techniques for detecting software errors, namely: Black Box and White Box Testing. Black Box testing technique is used for validation, namely whether you have built the right software and White Box testing is used for verification, namely whether the software made is correct. This study aims to test the value index software using three methods of Black Box testing techniques, namely Equivalence partitioning (EP), Boundary Value Analysis (BVA) and Error Guessing. The testing process is carried out to find out how much error occurs in the value index software. The research method is qualitative descriptive and analytical. The results of this study indicate that the value index application has no shortcomings (error of 0%) both with BVA, EP and Error Guessing techniques.

eHealth interventions are becoming a part of standard care, with software solutions increasingly created for patients and health care providers. Testing of eHealth software is important to ensure that the software realizes its goals. Software testing, which is comprised of alpha and beta testing, is critical to establish the effectiveness and usability of the software. In this viewpoint, we explore existing practices for testing software in health care settings. We scanned the literature using search terms related to eHealth software testing (eg, "health alpha testing," "eHealth testing," and "health app usability") to identify practices for testing eHealth software. We could not identify a single standard framework for software testing in health care settings; some articles reported frameworks, while others reported none. In addition, some authors misidentified alpha testing as beta testing and vice versa. There were several different objectives (ie, testing for safety, reliability, or usability) and methods of testing (eg, questionnaires, interviews) reported. Implementation of an iterative strategy in testing can introduce flexible and rapid changes when developing eHealth software. Further investigation into the best approach for software testing in health care settings would aid the development of effective and useful eHealth software, particularly for novice eHealth software developers.

Background: The development of technology, both hardware and software, is rapidly advancing due to the growth of Artificial Intelligence (AI), which simplifies many human activities, including the software testing process. This research journal discusses how software testing is conducted through a Quality Management System (QMS). Objective: The journal examines whether software testing processes are related to QMS, how significantly QMS supports software testing, and the differences between testing with QMS and without QMS. Methods: This study outlines the stages of software testing through QMS, such as Software Quality Assurance (SQA), Quality Control (QC), Test Maturity Model Integration (TMMI), and performance indicators. According to the American Society for Quality (ASQ), QMS is a formal and documented system consisting of policies, processes, and procedures designed to achieve an organization's quality policies and objectives. Results: Several experts also define QMS as a system that focuses on improving customer satisfaction, process consistency, efficiency, and the overall quality of products or services. Software, according to Roger S. Pressman, is a set of instructions (programs), data structures that allow information manipulation, and related documentation describing operations and functions. Conclusion: Overall, QMS not only improves software product quality but also strengthens the testing process, making it more optimal, efficient, and aligned with organizational goals. The integration of QMS frameworks such as ISO 9001:2015, CMMI, and TMMI provides organizations with a competitive advantage by ensuring consistent delivery of high-quality software products that meet or exceed customer expectations.