A Survey on Test Case Reduction Techniques

In software development, testing is widely used by developers to reveal faults that cause failures and improve the quality of the software by omitting or removing the detected faults. Test suites for testing and validating software programs can contain large numbers of test cases to execute various parts of the software program code to check for defects and code compliance. Test suite sizes may grow significantly with subsequent modifications to the software over time that causes redundancy in test suites. Due to time and resource constraints retesting the modified software every time, it is advantageous to develop techniques that manage test suite sizes by periodically removing duplicate test cases, in turn, can improve the maintenance efforts required for regression testing. The primary objective of test suite reduction is to effectively improve the testing process while maintaining Fault Detection Effectiveness and fulfilling all the testing requirements as well. Researchers have proposed some TSR heuristics based on different variations of a greedy algorithm. This paper presents a comparative study of test suite reduction techniques based on some parameters.

Test suite reduction is a critical activity which occurs pre or post test cases generation process. As software keeps growing large amounts of new test cases will be generated and added to the test pool and others will be updated. Accordingly test suite size will increase. Test suite reduction techniques have been pr oposed to eliminate redundant or irrelevant test cases based on variant criteria, while seeking to maintain the total effectiveness of the reduced test suite. In this paper we address and compare four test case reduction techniques: CBR (case-based reasoning) deletion algorithms Technique, GE & GRE Heuristics and priority cost technique, Model-checker based technique and Base choice coverage criterion technique. The aim of the study is to provide a guideline for choosing the appropriate test suite reduction techniques.

International Journal of Computer Applications, 2017

The cost of commercial software systems is usually over budget and within limited time duration. Most critical and time consuming phase of a software development process is effort estimation and 50% of this phase is often devoted to testing effort estimation. If we can find a way to reduce effort estimation, then it will greatly deduct total cost and effort needed to be spent in software development. Researchers have been continuously trying to find new ways to reduce effort estimation and improving methods which have been devised already. This paper proposes to use path-oriented test case generation and their reduction with reference to different priorities such as complexity, impact, path coverage etc. We are using different examples to reduce the test cases using various factors i.e. Test case Complexity, its impact and the path coverage.

Software test cases play an important role in Software Testing. A lot of test cases are developed while designing test cases that are of no use or redundant in nature. These types of test cases increase the effort and cost involved in testing and results in decrease of software testing efficiency. In this paper, a Genetic Algorithm based methodology has been proposed which can significantly reduce the test suite and leads to optimization of the efficiency of software testing. The testing efficiency can be increased by identifying the most critical paths. The proposed Genetic Algorithm based approach select the software path clusters that are weighted in accordance with the criticality of the path. Proposed algorithm has been evaluated by applying simple Genetic Algorithm and Fitness Scaling on a case study. GA with Fitness scaling helps in improving the efficiency of software testing by effectively reducing software test cases.

2006

As a test suite usually contains redundancy, a subset of the test suite (representative set) may still satisfy all the test objectives. As the redundancy increases the cost of executing the test suite, many test suite reduction techniques have been brought out in spite of the NP-completeness of the general problem of finding the optimal representative set of the test suite. In the literature, some experimental studies of test suite reduction techniques have already been reported, but there are still shortcomings of the studies of these techniques. This paper presents an experimental comparison of the four typical test suite reduction techniques: heuristic H, heuristic GRE, genetic algorithm-based approach and ILP-based approach. The aim of the study is to provide a guideline for choosing the appropriate test suite reduction techniques.

International Journal of Software Innovation, 2016

Software testing is a process of ratifying the functionality of software. It is a crucial area which consumes a great deal of time and cost. The time spent on testing is mainly concerned with testing large numbers of unreliable test cases. The authors' goal is to reduce the numbers and offer more reliable test cases, which can be achieved using certain selection techniques to choose a subset of existing test cases. The main goal of test case selection is to identify a subset of the test cases that are capable of satisfying the requirements as well as exposing most of the existing faults. The state of practice among test case selection heuristics is cyclomatic complexity and code coverage. The authors used clustering algorithm which is a data mining approach to reduce the number of test cases. Their approach was able to obtain 93 unique effective test cases out a total of 504.

Asian Journal of Computer Science and Technology, 2018

During software development, testing and re-testing occurs frequently to ensure that the software is working correctly before and after modifications. To carry out an effective testing process a test suite is created and executed to detect the faults in the existing code as well as in the modified code. The manual approach of test suite creation and execution is time consuming and labour intensive task as compared to automatically generated test data or test suite. The automatic test data generation is supposed to be an effective way, but a lot of redundant test cases are generated that increase the time, effort and cost of testing. Therefore, test suite minimization techniques are used to further minimize or reduce the number of test cases by selecting a subset from an initially random and large test suite to test the code before as well as after modification. In this study, a comprehensive analysis of the different test suite minimization techniques is presented in order to extend the existing studies and to propose new ideas in this direction.

Software testing is critical but most expensive phase of Software Development Life Cycle (SDLC). Development organizations desire to thoroughly test the software. But this exhaustive testing is impractical due to resource constraints. A large number of test suites are generated using automated tools. But the real challenge is the selection of subset of test cases and/or high order test cases crucial to validate the System Under Test (SUT). Test case reduction and prioritization techniques help test manager to solve this problem at a little cost. In this paper, we investigate their impact on testing process effectiveness using previous empirical studies. The results indicate that these techniques improve the testing effectiveness significantly. At the end, a case study is presented that suggests different useful combinations of these techniques which are helpful for different testing scenarios.

Indian Journal of Science and Technology, 2017

Objectives: Development of an efficient test suite minimization approach in order to reduce the size of a previously acquired test suite and produce a new representative suite which will guarantee the same requirement coverage that was achieved before minimization for an effective and efficient regression testing. Method: Test suite minimizations techniques try to reduce the size and redundancy of test suite by removing certain test cases since requirement covered by them are already covered by other test cases. But, it has been found that the acquired test cases after minimization severely lacks ability to achieve the desirable code coverage because the minimization was done based on a single test adequacy criteria. In this paper, we propose an efficient heuristic based test suite minimization algorithm which will reduce the size of the test suites with respective to multiple test adequacy criterions in order to preserve the fault detection effectiveness and code coverage characteristics of the final test suite. Findings: Our experimental results indicate that a significant percentage of reduction in the test suite size is achieved when the minimization is performed with respect to multiple test adequacy criterions. Our approach is unique compared to the existing approaches in the sense that, we carried out minimization based on multiple test adequacy criterions while most of the existing approaches usually take one or two criterions into consideration. The proposed approach is evaluated based on two well known software testing metrics; one indicate the percentage of reduction in test suite size and the second one indicate the percentage of code coverage achieved by the minimized test suite. Our experimental results indicate that a significant percentage of reduction in the size as well as significant code coverage characteristics is achieved when the minimization is done according to the proposed approach. Improvements: The important contribution of this study is that, it presents a novel and efficient test suite minimization technique that optimizes the test suite size based on multiple adequacy criterions.