Construction of Mixed Covering Arrays for Pair-wise Testing Using Probabilistic Approach in Genetic Algorithm

Sabharwal, Sangeeta; Bansal, Poonam; Mittal, Nitish; Malik, Shreya

doi:10.1007/s13369-015-2022-1

Cited by 8 publications

(8 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The TCAS model chooses the part of the aeronautical software system used to avoid collision between aeroplanes as the input space. The TCAS can be expressed as MCA(;t, 2 7 3 2 4 1 10 2 ). The results of the comparison between QSSMA and other methods, including the size and time (for qualitative reference only) of generating test suites, are shown in Table 4.…”

Section: Comparison Of Qssma With State State-of-the-artmentioning

confidence: 99%

See 1 more Smart Citation

A memetic algorithm for high‐strength covering array generation

et al. 2023

View full text Add to dashboard Cite

Covering array generation (CAG) is the key research problem in combinatorial testing and is an NP-complete problem. With the increasing complexity of software under test and the need for higher interaction covering strength t, the techniques for constructing highstrength covering arrays are expected. This paper presents a hybrid heuristic memetic algorithm named QSSMA for high-strength CAG problem. The sub-optimal solution acceptance rate is introduced to generate multiple test cases after each iteration to improve the efficiency of constructing high-covering strength test suites. The QSSMA method could successfully build high-strength test suites for some instances where t up to 15 within one day cutoff time and report five new best test suite size records. Extensive experiments demonstrate that QSSMA is a competitive method compared to state-of-theart methods. K E Y W O R D S optimisation, particle swarm optimisation, software engineering | INTRODUCTIONSoftware testing is an important area of software engineering and has great significance in ensuring software quality. Due to the large amount of test data required by modern software, if a complete test is performed, the size of the test data will increase sharply as increasing parameters or parameter values, leading to a lower testing efficiency. Thus, effective failure detection at a low cost is a critical issue for test case generation. Combinatorial testing (CT), a popular testing method, can detect failures triggered by various interactions of factors [1].There are two key issues in the field of combinatorial testing: (1) how to construct a test suite as small as possible; (2) how to find the combination of parameters that trigger the fault in a particular test case that triggers a software under test (SUT) failure. Researchers are more concerned with problem one, known as covering array generation (CAG) [2]. The main idea of CT is to construct a test suite represented by a covering array (CA) that contains all combinations of parameter's value of the SUT at least once and hopes the array is as small as possible. The generation of the smallest size test suite has been proven to be an NP-complete problem [3]. Numerous methods have been proposed, including mathematical, greedy, and artificial intelligence-based search methods. However, all the proposed mathematical methods apply only to restrictive parameter sets. Besides, greedy algorithms often result in oversized test suites. Due to this limitation, scholars focus more on AI-based (heuristic) search methods [4].Many efficient heuristic search algorithms have been successfully applied to CA generation, including geneticThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

show abstract

Section: Comparison Of Qssma With State State-of-the-artmentioning

confidence: 99%

“…algorithms (GA) [5][6][7], ant colony algorithm (ACA) [8,9], particle swarm optimisation (PSO) [10][11][12][13][14], simulated annealing (SA) [15,16], hill-climbing [17], harmony search algorithm (HS) [18,19], and cuckoo search (CS) [20][21][22].…”

mentioning

confidence: 99%

A memetic algorithm for high‐strength covering array generation

et al. 2023

View full text Add to dashboard Cite

show abstract

“…HS is a potent technique to produce the minimum array size for the test suite, although it is www.ijacsa.thesai.org slow. Some of the most important heuristic search algorithms include SA [9-11, 20, 21], GA [11,12,[22][23][24][25][26][27]62], ACA [8,12], PSO [7,[28][29][30], Cuckoo Search [3,33,34], Tabu Search (TS) [11,63] and Harmony Search (HS) [4,31,32]. Initially, most algorithms were implemented in order to produce pairwise interaction (2-way), as by Stardom [8] such as; SA, GA and TS.…”

Section: Heuristic Search Algorithmmentioning

confidence: 99%

“…In addition to SA and ACA [12], other strategies also are based on the GA algorithm with less modification in the structure which produces a good result regarding efficiency. These strategies are PWiesGen [25], PWiesGenPM [22], tuned Genetic (GS) [62] and Genetic Algorithm for Pairwise Test Sets (GAPTS) [26]. Notably, PWiesGenPM is better compared to GAPTS and GS is stronger than PWiesGenPM, GAPTS and PWiesGen.…”

Section: Heuristic Search Algorithmmentioning

confidence: 99%

“…By drawing inspiration from the SBSE technique, various strategies have already been suggested towards the problem of uniform and variable strength interaction (e.g. Simulated Annealing (SA) [9-11, 20, 21], Genetic Algorithm (GA) [12,[21][22][23][24][25][26][27], Practice Swarm Optimization (PSO) [7,[28][29][30], Ant Colony Algorithm (ACA) [8,12], Harmony Search Algorithm (HS) [4,31,32], Cuckoo Search (CS) [3,33,34], and the Bat Algorithm (BA) [35][36][37][38][39]). Even though relevant strategies have been found to be useful based on AI, they are noted in the literature as being slow due to costly computations [3,4,7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ABCVS: An Artificial Bee Colony for Generating Variable T-Way Test Sets

Alazzawi¹,

Rais²,

Basri³

2019

IJACSA

View full text Add to dashboard Cite

To achieve acceptable quality and performance of any software product, it is crucial to assess various software components in the application. There exist various softwaretesting techniques such as combinatorial testing and covering array. However, problems such as t-way combinatorial explosion is still challenging in any combinatorial testing strategy, as it takes into consideration the entire combinations of input variables. Therefore, to overcome this problem, several optimizations and metaheuristic strategies have been suggested. One of the most effective optimization algorithms based techniques is the Artificial Bee Colony (ABC) algorithm. This paper presents t-way generation strategy for both a uniform and variable strength test suite by applying the ABC strategy (ABCVS) to reduce the size of the test suite and to subsequently enhance the test suite generation interaction. To assess both the effectiveness and performance of the presented ABCVS, several experiments were conducted applying various sets of benchmarks. The results revealed that the proposed ABCVS outweigh the existing based strategies and demonstrated wider interaction between components as opposed to AI-search based and computational based strategies. The results also revealed higher prospect of ABCVS in the aspect of its effectiveness and performance as observed in the majority of case studies. 260 | P a g e www.ijacsa.thesai.org acceptable strategy, known as the Artificial Bee Colony Strategy (ABCVS) continuous to our previous research [40,41] for uniform and variable strength interaction t-way minimal test suite production. It is anticipated that ABCVS will address this problem. In fact, experimental results have revealed that the proposed ABCVS is able to support higher interaction strengths up to t = 6 compared to other Artificial Intelligence (AI)-based strategies. Furthermore, ABCVS it can compete against these other strategies in the majority of the case studies examined in the literature regarding efficiency (test suite generation) and performance (speed) and against other AI-based strategies having higher interaction strengths.The remainder of this paper is structured accordingly. Section II presents the background to this study, (i.e. CA and MCA concepts) which is followed by Section III which surveys "state of the art" testing strategies in this area. Section IV provides an overview of an ABC which is then followed by Section V describing the proposed strategy, consisting of two parts: (1) construction of the covering matrix, and (2) the proposed ABCVS. Section VI illustrates the tuning of the ABCVS parameters. Section VII evaluates the ABCVS by conducting several benchmark experiments in terms of effciency and performance alongside with statistical analysis evaluation by using Wilcoxon signed-rank test in Section VIII. Section IX discusses the advantages, limitations and threats to the validity of the approach, which followed by Section X providing overall conclusions to the study and presenting recommendations for future work. 12

show abstract