A concept analysis inspired greedy algorithm for test suite minimization

Abstract

Software testing and retesting occurs continuously during the software development lifecycle to detect errors as early as possible and to ensure that changes to existing software do not break the software. Test suites once developed are reused and updated frequently as the software evolves. As a result, some test cases in the test suite may become redundant as the software is modified over time since the requirements covered by them are also covered by other test cases. Due to the resource and time constraints for re-executing large test suites, it is important to develop techniques to minimize available test suites by removing redundant test cases. In general, the test suite minimization problem is NP complete. In this paper, we present a new greedy heuristic algorithm for selecting a minimal subset of a test suite T that covers all the requirements covered by T . We show how our algorithm was inspired by the concept analysis framework. We conducted experiments to measure the extent of test suite reduction obtained by our algorithm and prior heuristics for test suite minimization. In our experiments, our algorithm always selected same size or smaller size test suite than that selected by prior heuristics and had comparable time performance.

Selecting Highly Efficient Sets of Subdomains for Mutation Adequacy

“…The problem with this approach is that criteria met by earlier test cases are also often met by a combination of test cases selected later in the process, thus making some of the earlier test cases redundant [17]. Tallam et al [25] addressed this issue by removing redundant test cases before applying the greedy algorithm. In addition to this, Jeffrey and Gupta [26] use a second set of requirements to determine whether a test case really is redundant.…”

Bi-criteria Test Suite Reduction by Cluster Analysis of Execution Profiles

“…Related work in the context of test suite reduction can be classified into two main categories: The works in which a new technique is presented [3,4,5,16,9,6,7], and empirical studies on the previous techniques [1,2,15,17]. The works which propose a new approach commonly include heuristic algorithms [14], genetic algorithm-based techniques [18] and approaches based on integer linear programming [19].…”

“…A major difficulty with the regression testing is the excessive number of test cases accumulated while generating new test cases to test any new or modified functionality within the program [1]. To resolve the difficulty and to reduce the excessive cost of regression testing, various coverage-based and distribution-based techniques have been proposed [2,3,4,5,6,7,8]. These techniques attempt to permanently discard redundant test cases and retain the most effective ones to reduce the excessive cost of regression testing [6].…”

Incremental Model-based Test Suite Reduction with Formal Concept Analysis

“…In case there are multiple test scenarios covering the most and same amount of elements, one of the test scenarios will arbitrarily selected. However, greedy heuristic may not always provide the optimal test suite [12,14].…”