Regression Test Case Selection Research Articles

Retracted: A Systematic Analysis of Regression Test Case Selection: A Multi-Criteria-Based Approach

Retracted: A Systematic Analysis of Regression Test Case Selection: A Multi-Criteria-Based Approach

A novel chaotic archimedes optimization algorithm and its application for efficient selection of regression test cases

A novel chaotic archimedes optimization algorithm and its application for efficient selection of regression test cases

Systematic Literature Review on Test Case Selection and Prioritization: A Tertiary Study

Software testing is undertaken to ensure that the software meets the expected requirements. The intention is to find bugs, errors, or defects in the developed software so that they can be fixed before deployment. Testing of the software is needed even after it is deployed. Regression testing is an inevitable part of software development, and must be accomplished in the maintenance phase of software development to ensure software reliability. The existing literature presents a large amount of relevant knowledge about the types of techniques and approaches used in regression test case selection and prioritization (TCS&P), comparisons of techniques used in TCS&P, and the data used. Numerous secondary studies (surveys or reviews) have been conducted in the area of TCS&P. This study aimed to provide a comprehensive examination of the analysis of the enhancements in TCS&P using a thorough systematic literature review (SLR) of the existing secondary studies. This SLR provides: (1) a collection of all the valuable secondary studies (and their qualitative analysis); (2) a thorough analysis of the publications and the trends of the secondary studies; (3) a classification of the various approaches used in the secondary studies; (4) insight into the specializations and range of years covered in the secondary texts; (5) a comprehensive list of statistical tests and tools used in the area; (6) insight into the quality of the secondary studies based on the seven selected Research Paper Quality parameters; (7) the common problems and challenges encountered by researchers; (8) common gaps and limitations of the studies; and (9) the probable prospects for research in the field of TCS&P.

A Prioritization Approach for Regression Test Cases Based on a Revised Genetic Algorithm

The regression testing is a software-based testing approach executed to verify that changes made to the softwaredo not affect the existing functionality of the product. On account of the constraints of time and cost, it isimpractical to re-execute all the test cases for software whenever a change occurs. In order to overcome sucha problem in the selection of regression test cases, a prioritization technique should be employed. On the basisof some predefined criterion, the prioritization techniques create an execution schedule for the test cases, sothe higher priority test cases can be performed earlier than the lower priority test cases in order to improvethe efficiency of the software testing. Many prioritization criteria for regression test cases have been proposedin software testing literature; however, most of such techniques are code-based. Keeping in view this fact, thisresearch work has proposed a prioritization approach for regression test cases generated from software specificationswhich are based on the criterion of the Average Percentage Transition Coverage (APTC) by using arevised genetic algorithm. This criterion evaluates the rate of transitions coverage by incorporating knowledgeabout the significance of transitions between activates in the form of weights. APTC has been used as a fitnessevaluation function in a genetic algorithm to measure the effectiveness of a test cases sequence. Moreover, inorder to improve the coverage percentage, the proposed approach has revised the genetic algorithm by solvingthe problem of the optimal local solution. The experimental results show that the proposed approach demonstratesa good coverage performance with less execution time as compared to the standard genetic algorithmand some other prioritization techniques.

A Systematic Analysis of Regression Test Case Selection: A Multi-Criteria-Based Approach

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.

Performance comparison of Bat search and Cuckoo search using software artefact infrastructure repository and regression testing

Regression testing is conducted to ensure that no new errors have been introduced into the software as a result of the maintenance activity performed. Re-executing all the existing test cases is however a highly expensive and time consuming approach to gain this confidence. Previous research revealed that nature inspired algorithms have vast application in this area. In this paper, Bat search algorithm is tested against Cuckoo search algorithm to solve regression test case selection problem. Two factors: number of faults covered and computational time are considered for the comparison. Extensive experiments have been conducted over the objects adopted from benchmarked software artifact infrastructure repository. Rigorous statistical tests are conducted to draw a conclusion that Cuckoo search is marginally advantageous over Bat search algorithm with respect to performance parameters. We believe that the results reported in this paper will enable researchers to develop more powerful algorithm for testing in near future.

An Effective Regression Test Case Selection Using Hybrid Whale Optimization Algorithm

Test suite optimization is an ever-demanded approach for regression test cost reduction. Regression testing is conducted to identify any adverse effects of maintenance activity on previously working versions of the software. It consumes almost seventy percent of the overall software development lifecycle budget. Regression test cost reduction is therefore of vital importance. Test suite optimization is the most explored approach to reduce the test suite size to re-execute. This article focuses on test suite optimization as a regression test case selection, which is a proven N-P hard combinatorial optimization problem. The authors have proposed an almost safe regression test case selection approach using a Hybrid Whale Optimization Algorithm and empirically evaluated the same on subject programs retrieved from the Software Artifact Infrastructure Repository with Bat Search and ACO-based regression test case selection approaches. The analyses of the obtained results indicate an improvement in the fault detection ability of the proposed approach over the compared ones with significant reduction in test suite size.

Regression test case selection and prioritization for object oriented software

In software maintenance, after modifying the software a system needs regression testing. Execution of regression testing confirms that any modified code has no adverse effect as well as does not introduce new faults in the existing functionality of the software. When working with object-oriented programming code-based testing is generally expensive. In this study, we proposed a technique for regression testing using unified modeling language (UML) diagrams and code-based analysis for object-oriented software. In this research work, the design and code based technique with an evolutionary approach are presented to select the best possible test cases from the test suite. We used the dependency graph for intermediate representation for the objectoriented program to identify the change. The selection of test cases is done at the design level using the UML model. The models are compared to identify the change between these two models. The proposed approached maximizes the value of APFD.

HSP: A hybrid selection and prioritisation of regression test cases based on information retrieval and code coverage applied on an industrial case study

The usual way to guarantee quality of software products is via testing. This paper presents a novel strategy for selection and prioritisation of Test Cases (TC) for Regression testing. In the lack of code artifacts from where to derive Test Plans, this work uses information conveyed by textual documents maintained by Industry, such as Change Requests. The proposed process is based on Information Retrieval techniques combined with indirect code coverage measures to select and prioritise TCs. The aim is to provide a high coverage Test Plan which would maximise the number of bugs found. This process was implemented as a prototype tool which was used in a case study with our industrial partner (Motorola Mobility). Experiments results revealed that the combined strategy provides better results than the use of information retrieval and code coverage independently. Yet, it is worth mentioning that any of these automated options performed better than the previous manual process deployed by our industrial partner to create test plans.

SARLA – A 3-Tier Architectural Framework Based on the ACO for the Probablistic Analysis of the Regression Test Case Selection and Their Prioritization

SARLA – A 3-Tier Architectural Framework Based on the ACO for the Probablistic Analysis of the Regression Test Case Selection and Their Prioritization

SPIRITuS: a SimPle Information Retrieval regressIon Test Selection approach

Context:Regression Test case Selection (RTS) approaches aim at selecting only those test cases of a test suite that exercise changed parts of the System Under Test (SUT) or parts affected by changes.Objective:We present SPIRITuS (SimPle Information Retrieval regressIon Test Selection approach). It uses method code coverage information and a Vector Space Model to select test cases to be run. In a nutshell, the extent of a lexical modification to a method is used to decide if a test case has to be selected. The main design goals of SPIRITuS are to be: (i) easy to adapt to different programming languages and (ii) tunable via an easy to understand threshold.Method:To assess SPIRITuS, we conducted a large experiment on 389 faulty versions of 14 open-source programs implemented in Java. We were mainly interested in investigating the tradeoff between the number of selected test cases from the original test suite and fault detection effectiveness. We also compared SPIRITuS against well-known RTS approaches.Results:SPIRITuS selects a number of test cases significantly smaller than the number of test cases the other approaches select at the price of a slight reduction in fault detection capability.Conclusions:SPIRITuS can be considered a viable competitor of existing test case selection approaches especially when the average number of test cases covering a modified method increases (such information can be easily derived before test case selection takes place).

Effective Regression Test Case Selection

Regression test case selection techniques attempt to increase the testing effectiveness based on the measurement capabilities, such as cost, coverage, and fault detection. This systematic literature review presents state-of-the-art research in effective regression test case selection techniques. We examined 47 empirical studies published between 2007 and 2015. The selected studies are categorized according to the selection procedure, empirical study design, and adequacy criteria with respect to their effectiveness measurement capability and methods used to measure the validity of these results. The results showed that mining and learning-based regression test case selection was reported in 39% of the studies, unit level testing was reported in 18% of the studies, and object-oriented environment (Java) was used in 26% of the studies. Structural faults, the most common target, was used in 55% of the studies. Overall, only 39% of the studies conducted followed experimental guidelines and are reproducible. There are 7 different cost measures, 13 different coverage types, and 5 fault-detection metrics reported in these studies. It is also observed that 70% of the studies being analyzed used cost as the effectiveness measure compared to 31% that used fault-detection capability and 16% that used coverage.

Hierarchical regression test case selection using slicing

In this paper, we propose a novel regression test case selection approach by decomposing an object-oriented (OO) program into packages, classes, methods and statements that are affected by some modification made to the program. This decomposition is based on the proposed hierarchical slicing of an OO program. By mapping these decompositions to the existing test suite, we select a new reduced regression test suite and add some new test cases, if necessary, to retest the modified program. We apply hierarchical slicing on a suitable intermediate graph proposed for representing an OO program. This intermediate graph representation corresponds to all the possible dependences among the different parts of an OO program. We improve the scalability of the intermediate graph to a considerable extent by identifying and removing the redundant edges from the graph and thus detect the affected program parts in less time. The average reduction in time achieved for all the ten programs under experimentation is approximately 28.1%. The test cases that cover these affected parts of the program are then selected for regression testing. The average reduction in the number of test cases selected for regression testing of experimental programs is approximately 56.3%.

Hierarchical regression test case selection using slicing

In this paper, we propose a novel regression test case selection approach by decomposing an object-oriented (OO) program into packages, classes, methods and statements that are affected by some modification made to the program. This decomposition is based on the proposed hierarchical slicing of an OO program. By mapping these decompositions to the existing test suite, we select a new reduced regression test suite and add some new test cases, if necessary, to retest the modified program. We apply hierarchical slicing on a suitable intermediate graph proposed for representing an OO program. This intermediate graph representation corresponds to all the possible dependences among the different parts of an OO program. We improve the scalability of the intermediate graph to a considerable extent by identifying and removing the redundant edges from the graph and thus detect the affected program parts in less time. The average reduction in time achieved for all the ten programs under experimentation is approximately 28.1%. The test cases that cover these affected parts of the program are then selected for regression testing. The average reduction in the number of test cases selected for regression testing of experimental programs is approximately 56.3%.

Software Regression Test Case Prioritization for Object-Oriented Programs using Genetic Algorithm with Reduced-Fitness Severity

One of the most important activities in software maintenance is Regression Testing. The re-execution of all test cases during the regression testing is costly. And even though several of the code based proposed techniques by researchers address procedural programs. Some researchers addressed the issue of test case prioritization using Genetic Algorithm, but most of the authors do not select modification-revealing before prioritization, and used the same fault severity even when a fault was executed by the preceding test case. This paper propose a regression test case selection for object-oriented software based on dependence graph model analysis of the source code and optimizes the selected test case using Genetic Algorithm. The goal is to select modification-revealing test cases and order them based on their fitness value computed using previous history of the fault severity. The effectiveness of the approach was evaluated using Average Percentage of Rate of Fault Detection (APFD) metric with vending machine program. Our Proposed Approach (PA) provides considerably better results in term of average percentage of rate of fault detection. Based on the measured performance obtained from the results, GA with reduced severity of fault prioritize selected test cases more effectively compared to using GA with the same severity of fault and non-prioritize, which may result in reducing the cost of Regression Testing.

Coverage‐based regression test case selection, minimization and prioritization: a case study on an industrial system

SummaryThis paper presents a case study of coverage‐based regression testing techniques on a real world industrial system with real regression faults. The study evaluates four common prioritization techniques, a test selection technique, a test suite minimization technique and a hybrid approach that combines selection and minimization. The study also examines the effects of using various coverage criteria on the effectiveness of the studied approaches. The results show that prioritization techniques that are based on additional coverage with finer grained coverage criteria perform significantly better in fault detection rates. The study also reveals that using modification information in prioritization techniques does not significantly enhance fault detection rates. The results show that test selection does not provide significant savings in execution cost (<2%), which might be attributed to the nature of the changes made to the system. Test suite minimization using finer grained coverage criteria could provide significant savings in execution cost (79.5%) while maintaining a fault detection capability level above 70%, thus representing a possible trade‐off. The hybrid technique did not provide a significant improvement over traditional minimization techniques. Copyright © 2015 John Wiley & Sons, Ltd.

Study and Analysis of Regression Test Case Selection Techniques

activity of re-testing of only those parts of the program or code, in which some modifications are performed to ensure that errors have not been added and the changes do not affect the other parts of the code, which have not been modified is called as regression testing. Regression testing is essential as it reduces the size of the test suite, thus reducing the time and effort for testing. In this paper, different techniques for the regression test case selection for various programming paradigms are discussed.

A model-based regression test selection approach for embedded applications

Regression test selection techniques for embedded programs have scarcely been reported in the literature. In this paper, we propose a model-based regression test selection technique for embedded programs. Our proposed model, in addition to capturing the data and control dependence aspects, also represents several additional program features that are important for regression test case selection of embedded programs. These features include control flow, exception handling, message paths, task priorities, state information and object relations. We select a regression test suite based on slicing our proposed graph model. We also propose a genetic algorithm-based technique to select an optimal subset of test cases from the set of regression test cases selected after slicing our proposed model.

UML Modeling for Regression Testing of Component Based Systems

Selection of test cases for regression testing of Component-Based Software Systems is a difficult problem since source code of commercial-off-the-shelf (COTS) components are not often available. This paper demonstrates a UML model of regression testing of components. We use UML and Object-Constraint Language to specify selection of regression test cases based on version information.