Genetic Algorithm Based Test Data Generation for Structured Software Testing

Software industry produces high quality software, in that software testing plays a vital role. In software testing automatic generation can reduce the process time and cost. Automatic generation of object-oriented unit tests in software testing is a challenging task especially in white box testing. In software engineering Software testing becomes challenging issue with the development of software tools. Using Genetic Algorithm will provide best results in the software testing toolkit. This paper suggests an upgrading of automated search-based test generation of test cases in order to obtain high branch coverage in white box testing. The proposed technique outperforms providing greater efficiency in testing technique which combines Genetic Algorithm and Symbolic Execution named GASE. GASE automatically generates test data in order to obtain high branch coverage in white box testing aiming effective testing process which can be extended for software testing in cloud computing where it becomes necessity.

ESBMC, a bounded model checking (BMC) verifier based on SMT solving, has demonstrated its effectiveness in bug detection in recent software verification competitions. We extend its capabilities to enable branch coverage analysis and test suite generation. Our contributions are twofold: (1) we define a branch coverage property and instrument the control flow graph (CFG) to compute branch coverage using SMT solving, and (2) we propose an incremental multi-property reasoning algorithm for efficient and sound test case generation. ESBMC is ranked 7th in the category of Test-Comp 2025.

Genetic Algorithms (GAs) are adaptive search techniques that imitate the processes of evolution to solve optimization problems when traditional methods are considered too costly in terms of processing time and output effectiveness. In This research, we will use the concept of genetic algorithms to optimize the generation of test cases from the application user interfaces. This is accomplished through encoding the location of each control in the GUI graph to be uniquely represented and forming the GUI controls' graph. After generating a test case, the binary sequence of its controls is saved to be compared with future sequences. This is implemented to ensure that the algorithm will generate a unique test case or path through the GUI flow graph every time.

Cost-effective test case generation with the hyper-heuristic for software product line testing

Various software engineering paradigms and real-time projects have proved that software testing is the most critical and highly important phase in the SDLC. In general, software testing takes approximately 40–60% of the total effort and time involved in project development. Generating test cases is the most important process in software testing. There are many techniques involved in the automatic generation of these test cases which aim to find a smaller group of cases that could allow for an adequacy level to be achieved which will hence reduce the effort and cost involved in software testing. In the structural testing of a product, the auto-generation of test cases that are path focused in an efficient manner is a challenging process. These are often considered optimization problems and hence search-based methods such as genetic algorithm (GA) and swarm optimizations have been proposed to handle this issue. The significance of the study is to address the optimization problem of automatic test case generation in search-based software engineering. The proposed methodology aims to close the gap of genetic algorithms acquiring local minimum due to poor diversity. Here, dynamic adjustment of cross-over and mutation rate is achieved by calculating the individual measure of similarity and fitness and searching for the more global optimum. The proposed method is applied and experimented on a benchmark of five industrial projects. The results of the experiments have confirmed the efficiency of generating test cases that have optimum path coverage.

Software testing is the key to ensuring software quality. Test case generation and software fault localization are two important research objects of testing. Spectrum-based fault localization (SBFL) method is a mainstream dynamic fault localization method, which inputs test cases into the program for execution and collects statement coverage information and execution results. By analysing this information, a fault report can be generated. The performance of SBFL is affected by test case quality. Therefore, this paper proposes a test case generation method based on improved genetic algorithm to assist SBFL. This method uses a small number of initial test cases to execute the program, then uses the XGBoost model to pre-process the spectrum information and generates a list of potential fault statements to guide the iterative process of the genetic algorithm. The experimental results show that the test cases generated using the improved genetic algorithm have certain advantages over the test cases generated by general genetic algorithm and the baseline test cases.

Search-based techniques have been widely used for white-box test generation. Many of these approaches rely on the approach level and branch distance heuristics to guide the search process and generate test cases with high line and branch coverage. Despite the positive results achieved by these two heuristics, they only use the information related to the coverage of explicit branches (e.g., indicated by conditional and loop statements), but ignore potential implicit branchings within basic blocks of code. If such implicit branching happens at runtime (e.g., if an exception is thrown in a branchless-method), the existing fitness functions cannot guide the search process. To address this issue, we introduce a new secondary objective, called Basic Block Coverage (BBC), which takes into account the coverage level of relevant basic blocks in the control flow graph. We evaluated the impact of BBC on search-based unit test generation (using the DynaMOSA algorithm) and search-based crash reproduction (using the STDistance and WeightedSum fitness functions). Our results show that for unit test generation, BBC improves the branch coverage of the generated tests. Although small (sim 1.5%), this improvement in the branch coverage is systematic and leads to an increase of the output domain coverage and implicit runtime exception coverage, and of the diversity of runtime states. In terms of crash reproduction, in the combination of STDistance and WeightedSum, BBC helps in reproducing 3 new crashes for each fitness function. BBC significantly decreases the time required to reproduce 43.5% and 45.1% of the crashes using STDistance and WeightedSum, respectively. For these crashes, BBC reduces the consumed time by 71.7% (for STDistance) and 68.7% (for WeightedSum) on average.

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.

To generate efficient test data is one of the major problems during testing phase. This task is complex and very time consuming and researchers proposed different methods for generating the test data. This paper proposed a technique that uses the source code of the program, transforms it into Control Flow Graph (CFG), thereafter calculate the outdegree and then apply Genetic Algorithm over it to generate valuable test data during testing phase. The advantage of using the concept of outdegree in CFG is that it simplifies the technique of calculating the fitness function and reduces the overall testing time. The proposed technique not only simplify the overall task of applying Genetic Algorithm operators over test data but also reduces the complexity and testing time thereby increasing the efficiency of the technique.

The use of metaheuristic search techniques for the automatic generation of test data has been a burgeoning interest for many researchers in recent years. Previous attempts to automate the test gene...

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.

Test coverage is an important step in software testing and a good indicator of software quality. It helps in evaluating the effectiveness of testing by providing data on different coverage items. It ensures that testing is effectively carried out without missing out all the key functional areas or the features of the software under development. We have analyzed several approaches to understand the existing test coverage framework. These approaches have been classified in different categories namely instrumentation based (source code and byte code), information retrieval (latent semantic indexing), genetic algorithm and UML based. Moreover, we have compared these approaches based on some criteria such as coverage types, data base support, software traceability support, regression testing support, coverage construction and coverage re-construction techniques. We are of the opinion, although much research effort has been put on how to get coverage information by either code based testing or requirement based testing, not much has been paid to measure the coverage by combining both to get better coverage.

With people pay more and more attention to component software testing, the generation of test case is as one of the important works of software testing, it becomes a hotspot inevitably. For component software testing, making combination of the testing method based on model and testing method based on Genetic Algorithm, this paper proposes a generating method of test case which based on UML (Unified Modeling Language) activity diagram and Genetic Algorithm, and gives the overall design and implementation steps of the method. Then this paper applies the proposed method to model the adding table business in catering management system, and design test case. The example verifies the feasibility and validity of the method.

Software testing refers to a process that improves the quality of software systems through bug detection, but software testing is one of the time and cost-consuming stages in software development. Hence, software test automation is regarded as a solution, which can facilitate heavy and laborious tasks of testing. Problem: Automatic generation of data with maximum coverage of program branches is regarded as an NP-complete optimization problem. Several heuristic algorithms have been proposed for this problem. Failure to maximise branch coverage, the poor success rate in optimal test data generation, and low stable results are the major demerits of the previous methods. Goal: Enhancing the branch coverage rate of the generated test data, enhancing the success rate in generating the test data with maximum coverage, and enhancing the stability and speed criteria are the main goals of this study. Method: In this study, a combination of grey wolf optimization algorithm and genetic algorithm have been used to automatically generate optimal test data. The proposed hybrid method (Fuzuli <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> ) tries to generate test data with maximum branch coverage at the software source code level. Results: The results obtained from the proposed algorithm were compared with those of the following algorithms: Shuffled Frog Leaping Algorithm (SFLA), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA).The results obtained from running a wide range of tests on standard benchmark programs showed that the proposed algorithm outperforms other algorithms with an average coverage of %99.98, a success rate of %99.97, and an average output of 2.86.

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