An Efficient Regression Test Cases Selection & Optimization Using Mayfly Optimization Algorithm

Regression testing is unavoidable maintenance activity that is performed several times in software development life cycle. Optimization of regression test case is required to minimize the test case (which will in-turn reduce the time and cost of testing) and to find the fault in early testing activity. The two widely used regression test case optimization techniques, namely, selection and prioritization are recently found to be integrated with different metaheuristic algorithms for fruitful regression test cases. Among the various meta-heuristic algorithms, Ant colony optimization (ACO) algorithm is most popularly used. ACO will try to find the smallest path out all the test cases and it is not sufficient because it will not cover all the test cases which are needed. In this paper we have proposed a modified ant colony optimization to solve test cases in huge search space. The modified algorithm selects the best test cases that find the maximum fault in minimum time.

Fault localization remains a vital yet resource-intensive task, particularly within software evolution, where swift and accurate fault localization is crucial. Whereas substantial research has improved fault localization techniques, challenges persist specifically in evolving software systems. Simultaneously, regression testing has been investigated to enhance testing efficiency while maintaining fault localization accuracy. Despite these advancements, further methodological improvements are necessary to refine fault localization accuracy in evolving software environments. This study proposes a new combination of features extracted from the source code and the associated test suite. The features include coverage, data and control dependencies, and code changes, with a new feature, “Test Case Weight,” derived from the test suite. “Test Case Weight” incorporates the effects of regression testing, which is influenced by two regression testing techniques: test case reduction and selection. Consequently, this feature is entirely influenced by regression testing. The results show that the proposed method achieves over 81% fault localization in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Top-3</i> rankings. It also improves <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EXAM</i> score, with over 68% of programs scoring 10 or less via test case selection and 63% via test case reduction. Compared to spectrum-based techniques, the proposed method located 54% more faults than <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tarantula</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ochiai</i>, and 45% more than <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Jaccard</i> in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Top-3</i> rankings. Additionally, it outperforms existing methods in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EXAM</i> score while using only 40% to 80% of the test suite. These findings highlight the importance of incorporating historical data, code structure, runtime behavior, and test case to improve fault localization efficiency in evolving software.

Regression testing (RT) is an essential but an expensive activity in software development. RT confirms that new faults/errors will not have occurred in the modified program. RT efficiency can be improved through an effective technique of selected only modified test cases that appropriate to the modifications within the given time frame. Earlier, several test case selection approaches have been introduced, but either these techniques were not sufficient according to the requirements of software tester experts or they are ineffective and cannot be used for available test suite specifications and architecture. To address these limitations, we recommend an improved and efficient test case selection (TCS) algorithm for RT. Our proposed technique decreases the execution time and redundancy of the duplicate test cases (TC) and detects only modified changes that appropriate to the modifications in test cases. To reduce execution time for TCS, evaluation results of our proposed approach are established on fault detection, redundancy and already executed test case. Results indicate that proposed technique decreases the inclusive testing time of TCS to execute modified test cases by, on average related to a method of Hybrid Whale Algorithm (HWOA), which is a progressive TCS approach in regression testing for a single product.

Regression testing is an important process during software development. In order to reduce costs of regression testing, research on optimization of scheme of regression testing have been done in this paper. For the purpose of reducing the number of test cases and detecting faults of programs early, this paper proposed to combine test case selection with test case prioritization. Regression testing process has been designed and optimization of testing scheme has been implemented. The criterion of test case selection is modify impact of programs, finding programs which are impacted by program modification according to modify information of programs and dependencies between programs. Test cases would be selected during test case selection. The criterion of test case prioritization is coverage ability and troubleshooting capabilities of test case. Test cases which have been selected during test case selection would be ordering in test case prioritization. Finally, the effectiveness of the new method is discussed.

Time and resources are usually neglected areas in the life cycle of software development. So, these become the primary constraints in software testing. Optimization of a test suite is quite crucial in reducing the complexity of the testing phase and selection of the test cases by eliminating redundant data; this is critical for defining the strategies. Most of the work in literature employs single-objective optimization methods. Though these are not always efficient, these play a critical role in the selection of a test case. Test case selection is, however, non-deterministic. Selection of test cases using Parallel Programming is treated as a complex task due to the need for higher performance in Parallel Computing. Parallel Computing can be stated as a combination of Computational mechanisms and Mathematical techniques. Hence, this investigation proposes a novel BAT algorithm for multi-objective optimization. It has code coverage as well as Object-oriented testing strategies. Comparing the experimental results with the Genetic Algorithm (GA), it is observed that the proposed method has faster convergence with adequate code coverage.

Testing in Continuous Integration (CI) involves test case prioritization,\nselection, and execution at each cycle. Selecting the most promising test cases\nto detect bugs is hard if there are uncertainties on the impact of committed\ncode changes or, if traceability links between code and tests are not\navailable. This paper introduces Retecs, a new method for automatically\nlearning test case selection and prioritization in CI with the goal to minimize\nthe round-trip time between code commits and developer feedback on failed test\ncases. The Retecs method uses reinforcement learning to select and prioritize\ntest cases according to their duration, previous last execution and failure\nhistory. In a constantly changing environment, where new test cases are created\nand obsolete test cases are deleted, the Retecs method learns to prioritize\nerror-prone test cases higher under guidance of a reward function and by\nobserving previous CI cycles. By applying Retecs on data extracted from three\nindustrial case studies, we show for the first time that reinforcement learning\nenables fruitful automatic adaptive test case selection and prioritization in\nCI and regression testing.\n

Background: In today’s era, modifications in a software is a common requirement by customers. When changes are made to existing software, re-testing of all the test cases is required to ensure that the newly introduced changes do not have any unwanted effect on the behavior of the software. However, retesting of all the test cases would not only be time consuming but also expensive. Therefore, there is a need for a technique that reduces the number of tests to be performed. Regression testing is one of the ways to reduce the number of test cases. Selection technique is one such method which seeks to identify the test cases that are relevant to some set of recent changes. Objective: It is evident that most of the studies have used different selection techniques and have focused only on one parameter for achieving reduced test suite size without compromising the performance of regression testing. However, to the best of our knowledge, no study has taken two or more parameters of coverage, and/or execution time in a single testing. This paper presents a hybrid technique that combines both regression test selection using slicing technique and minimization of test cases using modified firefly algorithm with combination of parameters coverage and execution time in a single testing. Methods: A hybrid technique has been described that combines both selection and minimization. Selection of test cases is based upon slicing technique while minimization is done using firefly algorithm. Hybrid technique selects and minimizes the test suite using information on statement coverage and execution time. Results: The proposed technique gives 43.33% much superior result as compared to the other hybrid approach in terms of significantly reduced number of test cases. It shows that the resultant test cases were effective enough to cover 100% of the statements, for all the programs. The proposed technique was also tested on four different programs namely Quadratic, Triangle, Next day, Commission respectively for test suite selection and minimization which gave comparatively superior result in terms of reduction (%) in number of test cases required for testing. Conclusion: The combination of parameters used in slicing based approach, reduces the number of test cases making software testing an economical, feasible and time saving option without any fault in the source code. This proposed technique can be used by software practitioners/experts to reduce time, efforts and resources for selection and minimization of test cases.

The process of regression testing is generally done under critical time and resource constraints. In the existing approaches, Test Case Prioritization (TCP), Test Case Selection (TCS), and Test Suite Reduction (TSR) have been treated as different optimization problems. This is based on simple heuristics that do not use valuable information that can be extracted from the program under test. Moreover, they do not allow for making internally consistent budget-conscious decisions. This paper introduces a unified approach that combines various variants of Graph Neural Networks (GNNs) and Reinforcement Learning (RL) for the solutions of TCP, TCS, and TSR. In our proposal, the representation of regression artifacts such as test cases, code entities, and faults is used as nodes in the typed graph, while the edges are used to show the relations between these artifacts. The relation-aware GNN is used for generating test case embeddings, reflecting the distance between the test cases and the changed areas as well as the areas known to have issues in the past. The actor-critic RL agent uses these test case embeddings and the budget to decide whether to run, skip, or discard test cases. The performance of our proposal outstrips coverage-based heuristics, history-based ranking, a GA-based search, an enhanced QPSO, and two learning-based ablations, as validated by our experiments on four Java projects from Defects4J. In terms of various programs and budgets, using GNN–RL has led to an improvement in the Average Percentage of Faults Detected (APFD) and Cost-cognizant Average Percentage of Faults Detected (APFDc) by approximately 6–9 percentage points on lower budgets. In addition, it is possible to obtain a 42% suite reduction and a 48% cost reduction while still retaining 98% of all fault-revealing tests in the final suite. The results show that it is possible to obtain promising solutions for adaptive regression testing using budget-aware reinforcement learning and graph-based representation learning.

In applied software engineering, the algorithms for selecting the appropriate test cases are used to perform regression testing. The key objective of this activity is to make sure that modification in the system under test (SUT) has no impact on the overall functioning of the updated software. It is concluded from the literature that the efficacy of the test case selection solely depends on the following metrics, namely, the execution cost of the test case, the lines of the code covered in unit time also known as the code coverage, the ability to capture the potential faults, and the code modifications. Furthermore, it is also observed that the approaches for the regression testing developed so far generated results by focusing on one or two parameters. In this paper, our key objectives are twofold: one is to explore the importance of the role of each metric in detail. The secondary objective is to study the combined effect of these metrics in test case selection task that is capable of achieving more than one objective. In this paper, a detailed and comprehensive review of the work related to regression testing is provided in a very distinct and principled way. This survey will be useful for the researchers contributing to the field of regression testing. It is noteworthy that our systematic literature review (SLR) included the noteworthy work published from 2007 to 2020. Our study observed that about 52 relevant studies focused on all of the four metrics to perform their respective tasks. The results also revealed that about 30% of the different categories of regression test case reported the results using metaheuristic regression test selection (RTS). Similarly, about 31% of the literature reported results using the generic regression test case selection techniques. Most of the researchers focus on the datasets, namely, Software-Artefact Infrastructure Repository (SIR), JodaTime, TreeDataStructure, and Apache Software Foundation. For validation purpose, following parameters were focused, namely, the inclusiveness, precision, recall, and retest-all.

A software needs to be updated to survive in the customers’ ever-changing demands and the competitive market. The modifications may produce undesirable changes that require retesting, known as regression testing, before releasing it in the public domain. This retesting cost increases with the growth of the software test suite. Thus, regression testing is divided into three techniques: test case prioritization, selection, and minimization to reduce costs and efforts. The efficiency and effectiveness of these techniques are further enhanced with the help of optimization techniques. Therefore, we present the regression testing using well-known algorithms, genetic algorithm, particle swarm optimization, a relatively new nature-inspired approach, gravitational search algorithm, and its hybrid with particle swarm optimization algorithm. Furthermore, we propose a tri-level regression testing, i.e., it performs all the three methods in succession. Nature-inspired algorithms prioritize the test cases on code coverage criteria. It is followed by selecting the modification-revealing test cases based on the proposed adaptive test case selection approach. The last step consists of the removal of redundant test cases. The hybrid algorithm performed well for the average percentage of statement coverage, and the efficiency of genetic algorithm and particle swarm optimization is better comparatively. The proposed test case selection method can select at least 75% modification-revealing test cases using nature-inspired algorithms. Additionally, it minimizes the test suite with full statement coverage and almost negligible fault coverage loss. Overall, the simulation results show that the proposed hybrid technique outperformed the other algorithms.

Background &amp; 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 &amp; 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 &amp; 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

Regression testing is essential to ensure software quality during software evolution. Two widely-used regression testing techniques, test case selection and prioritization, are used to maximize the value of the continuously enlarging test suite. However, few works consider both these two techniques together, which decreases the usefulness of the independently studied techniques in practice. In the presence of changes during program evolution, regression testing is usually conducted by selecting the test cases that cover the impact results of the changes. It seldom considers the false-positives in the information covered. Hence, the effectiveness of such regression testing techniques is decreased. In this paper, we propose an approach, ComboRT, which combines test case selection and prioritization together to directly generate a ranked list of test cases. It is based on the impact results predicted by the change impact analysis (CIA) technique, FCA–CIA, which generates a ranked list of impacted methods. Test cases which cover these impacted methods are included in the new test suite. As each method predicted by FCA–CIA is assigned with an impact factor value corresponding to the probability of this method to be impacted, test cases are then ordered according to the impact factor values of the impacted methods. Empirical studies on four Java based software systems demonstrate that ComboRT can be effectively used for regression testing in object-oriented Java-based software systems during their evolution.

SPIRITuS: a SimPle Information Retrieval regressIon Test Selection approach

Test case selection for black-box regression testing of database applications

Software maintenance is performed regularly for enhancing and adapting the functionalities of the existing software, which modifies the software and breaks the previously verified functionalities. This sets a requirement for software regression testing, making it a necessary maintenance activity. As the evolution of software takes place the size of the test suite tends to grow, which makes it difficult to execute the entire test suite in a time constrained environment. There are many existing techniques for regression test case selection. Some are based on dataflow analysis technique, slicing-based technique, bio-inspired techniques, and genetic algorithm based techniques. This paper gives a regression test case selection technique based on fuzzy model, which reduces the size of the test suite by selecting test cases from existing test suite. The test cases, which are necessary for validating the recent changes in the software and have the ability to find the faults and cover maximum coding under testing in minimum time, are selected. A fuzzy model is designed which takes three parameters namely code covered, execution time and faults covered as input and produces the estimation for the test case selection probability as very low, low, medium, high and very high.