When to Release and Stop Testing of a Software

Significant advances in the field of software testing have been made by providing extended warranties, operational support and desired software reliability to the customers. However, the trade-off between minimising testing cost and maximising reliability to ensure well-timed market penetration has been a key concern for developers. Separate release and testing stop time strategy in order to optimise overall testing and market opportunity cost is proposed. We propose a generalised framework for developers to achieve multiple objectives of minimum testing and market opportunity cost; optimal warranty length; and optimal testing and release time. Software reliability growth models (SRGMs) are used to model an average number of bugs detected by testers (users) during the pre (post) release of software. Significant implications and quite intriguing facts related to separate release and testing stop time; early release ensuring higher reliability and; capitalising market opportunity to minimise overall testing cost are suggested based on proposed optimisation models. Numerical illustration is provided to validate the proposed framework.

A software reliability growth model incorporating the amount of test effort expenditures during the software testing phase is described. The time-dependent behavior of test-effort expenditures is described by a Weibull curve due to the flexibility. Assuming that the error detection rate to the amount of test-effort spent during the testing phase is proportional to the current error content, the model is formulated by a nonhomogeneous Poisson process. Using this model, the method of data analysis for software reliability measurement is presented. Also, this model is applied to the prediction of additional test-effort expenditures to obtain the objective number of errors detected by software testing and the determination of the optimum time to stop software testing for release. >

A large percentage of the cost of rework can be avoided by finding more faults earlier in a software test process. Therefore, determination of which software test phases to focus improvement work on has considerable industrial interest. We evaluate a number of prediction techniques for predicting the number of faults slipping through to unit, function, integration, and system test phases of a large industrial project. The objective is to quantify improvement potential in different test phases by striving toward finding the faults in the right phase. The results show that a range of techniques are found to be useful in predicting the number of faults slipping through to the four test phases; however, the group of search-based techniques (genetic programming, gene expression programming, artificial immune recognition system, and particle swarm optimization---based artificial neural network) consistently give better predictions, having a representation at all of the test phases. Human predictions are consistently better at two of the four test phases. We conclude that the human predictions regarding the number of faults slipping through to various test phases can be well supported by the use of search-based techniques. A combination of human and an automated search mechanism (such as any of the search-based techniques) has the potential to provide improved prediction results.

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

Effective prediction of software modules, those that are prone to defects, will enable software developers to achieve efficient allocation of resources and to concentrate on quality assurance activities. The process of software development life cycle basically includes design, analysis, implementation, testing, and release phases. Generally, software testing is a critical task in the software development process wherein it is to save time and budget by detecting defects at the earliest and deliver a product without defects to the customers. This testing phase should be carefully operated in an effective manner to release a defect-free (bug-free) software product to the customers. In order to improve the software testing process, fault prediction methods identify the software parts that are more noted to be defect-prone. This paper proposes a prediction approach based on conventional radial basis function neural network (RBFNN) and the novel adaptive dimensional biogeography based optimization (ADBBO) model. The developed ADBBO based RBFNN model is tested with five publicly available datasets from the NASA data program repository. The computed results prove the effectiveness of the proposed ADBBO-RBFNN classifier approach with respect to the considered metrics in comparison with that of the early predictors available in the literature for the same datasets.

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.

Today’s customer expectations to have well-developed complex enterprise level software, delivered within no time, have left developers with a dilemma as for how to attain the desired level of software reliability with shortened testing time? In this paper, we examine a two-dimensional testing time and effort-based model using Cobb-Douglas production function, for separate release and testing stop time strategy for software in order to optimize overall testing and market opportunity cost. Here we propose a generalized framework for software developers to achieve multiple objectives of minimizing overall testing and market opportunity cost, optimizing warranty length, and optimizing release and testing stop time. We make use of software reliability growth models (SRGMs) to model an average number of bugs to be detected by testers (users) during the pre-/(post)release of the software. Numerical illustration based on real-life Tandem Computers data set by considering exponential distribution along with the sensitivity of important parameters is provided to validate the proposed two-dimensional cost modeling framework.

Software testing (debugging) in order to reduce costs in terms of changes in the software reliability and testing costs, need to know in advance is more efficient. In this paper, proposes the reliability model with monotonic intensity functions (Power-Law, Musa-Okumoto and Comperz model), which made out efficiency application for software reliability. Algorithm to estimate the parameters used to maximum likelihood estimation and bisection method, model selection based on mean square error and coefficient of determination, for the sake of efficient model, were employed. Analysis of failure using real data set for the sake of proposing monotonic intensity function was employed. This analysis of failure data compared with monotonic intensity function. In order to insurance for the reliability of data, Laplace trend test was employed. In this study, the proposed monotonic intensity function is more efficient in terms of reliability in this area. Thus, monotonic intensity function can also be used as an alternative model. From this paper, software developers have to consider the growth model by prior knowledge of the software to identify failure modes which can be able to help. Keywords: Laplace Trend Test, Monotonic Intensity Function, NHPP, Software Reliability

This chapter describes a pragmatic approach to using open source and free software tools as valuable resources to affect learning of software industry practices using iterative and incremental development methods. The authors discuss how the above resources are used in teaching undergraduate Software Engineering (SE) courses. More specifically, they illustrate iterative and incremental development, documenting software requirements, version control and source code management, coding standards compliance, design visualization, software testing, software metrics, release deliverables, software engineering ethics, and professional practices. The authors also present how they positioned the activities of this course to qualify it for writing intensive designation. End of semester course evaluations and anecdotal evidence indicate that the proposed approach is effective in educating students in software industry practices.

An important activity in component-based software development is the testing of each component in its context of use. Since a component developer cannot anticipate all possible applications of the component, this task is usually left to the component user. What makes testing of component-based software especially challenging for the user is the lack of source code and partial knowledge of the working of the component. To ensure high quality of the overall software, the component user must perform testing tasks, i.e., generate test cases, create test oracles, evaluate coverage, and perform regression testing, all without access to the component’s source code or complete semantic knowledge. The developer of the component can facilitate the execution of these testing tasks by augmenting the component appropriately with information that improves its testability. This chapter describes techniques that can be employed by developers to build testable COTS components. A taxonomy of these techniques based on the phases of software testing and the role of the component developer/user is presented, followed by a description of an important subset of the following techniques: (1) State Machine Models, (2) Built-In Tests, (3) Built-In Contract Testing, (4) Interface Probing, (5) Traceable Components, (6) Metacontent, (7) Binary Reverse Engineering, (8) Fault Injection Support, (9) Testable Beans, (10) Retro Components, (11) Component Test Bench, and (12) Self-Testing Components. Finally, a discussion summing up all the techniques is presented.

The quality of the software is greatly determined by the testing phase, which involves various test cases that can be conducted through manual testing and automation testing. Manual testing is performed manually without using automation scripts, whereas automation testing is conducted using automation scripts. ABC is a company that operates globally in the field of access control, with the Card Printer being one of the menus used in access control. In the development process of this software, both manual and automation testing phases are carried out. The automation testing process employs the Selenium tool, which has proven to be time-consuming and poses challenges when running numerous test cases. This research aims to develop automation testing using Playwright to address the long execution time issue encountered with Selenium. The research utilizes the Card Printer project in the development of automation testing and adopts the Agile methodology. The result of developing automation testing using Playwright was successfully applied to 12 test cases. Additionally, the time analysis between Playwright and Selenium showed that Playwright has a total execution time of 4.9 minutes, which is faster compared to Selenium's total execution time of 8.3 minutes. With faster execution times, Playwright can be considered a tool in the development of automation testing.

Accurate reliability estimates can be obtained by using software reliability models only in the later phase of software testing. However, for cost effective and timely, management prediction in the early phase is important. Non-homogenerous Poisson process (NHPP) models and Artificial Neural Network (ANN) models are the most important Analytical software reliability growth models. In this paper we study an approach to using past fault-related data with Wavelet Networks model to improve reliability predictions in the early testing phase. A numerical example is shown with both actual and simulated datasets. The analysis with example shows that the proposed approach works effectively in the early phase of software testing.

The optimum release time or total testing time of a software product subject to the desired quality and total testing cost is an important issue. In this paper an attempt is made to extend the software cost model proposed by William et el.[1] by considering the cost factors such as test effort, cost of imperfect rectification of errors and life time warranty cost. The cost of software testing is the sum of the cost incurred due to initial testing cost, cost of testing the software per unit time and warranty cost. For this model the optimum release time and optimum release policy are proposed by minimizing the cost function subject to the desired reliability levels under the situation: (i) when warranty is provided to retain the reliability level promised at the time of software release (ii) when warranty is provided to increase the reliability level from the time of software release.

As the complexity and reliability requirements of software projects increase constantly, software testing is becoming more and more prominent and important in software engineering. Testing management system is the basis of constructing software testing management system. It regulates software testing processes, and ensures the entire testing process is under control. Based on the Django framework, this paper design and implement a test management system based on B/S architecture. It can operate as a common platform for software testing management, and reduce the cost and cycle of software testing, guarantee the quality and efficiency of software testing.

This paper presents a new stochastic model for determining the optimal release time for a computer software in testing phase, taking account of the debugging time lag. In the earlier works, most of software release models were considered, but it was assumed that an error detected can be removed instantaneously. In other words, none discussed quantitatively the effect of the software maintenance action in the optimal software release time. Main purpose of this work is to relate the optimal software release policy with the arrival-service process on the software operation phase by users. We use the Non-Homogeneous Poisson Process (NHPP) type of software reliability growth models as the software error detection phenomena and obtain the optimal software release policies minimizing the expected total software costs. As a result, the usage circumstance of a software in operation phase gives a monotone effect to the software release planning.