A Survey of Constrained Combinatorial Testing

Combinatorial Testing (CT) is a potentially powerful testing technique, whereas its failure revealing ability might be dramatically reduced if it fails to handle constraints in an adequate and efficient manner. To ensure the wider applicability of CT in the presence of constrained problem domains, large and diverse efforts have been invested towards the techniques and applications of constrained combinatorial testing. In this paper, we provide a comprehensive survey of representations, influences, and techniques that pertain to constraints in CT, covering 129 papers published between 1987 and 2018. This survey not only categorises the various constraint handling techniques, but also reviews comparatively less well-studied, yet potentially important, constraint identification and maintenance techniques. Since real-world programs are usually constrained, this survey can be of interest to researchers and practitioners who are looking to use and study constrained combinatorial testing techniques

Practical Combinatorial Interaction Testing: Empirical Findings on Efficiency and Early Fault Detection

Combinatorial interaction testing (CIT) is important because it tests the interactions between the many features and parameters that make up the configuration space of software systems. Simulated Annealing (SA) and Greedy Algorithms have been widely used to find CIT test suites. From the literature, there is a widely-held belief that SA is slower, but produces more effective tests suites than Greedy and that SA cannot scale to higher strength coverage. We evaluated both algorithms on seven real-world subjects for the well-studied two-way up to the rarely-studied six-way interaction strengths. Our findings present evidence to challenge this current orthodoxy: real-world constraints allow SA to achieve higher strengths. Furthermore, there was no evidence that Greedy was less effective (in terms of time to fault revelation) compared to SA; the results for the greedy algorithm are actually slightly superior. However, the results are critically dependent on the approach adopted to constraint handling. Moreover, we have also evaluated a genetic algorithm for constrained CIT test suite generation. This is the first time strengths higher than 3 and constraint handling have been used to evaluate GA. Our results show that GA is competitive only for pairwise testing for subjects with a small number of constraints

Comparative Analysis of Constraint Handling Techniques for Constrained Combinatorial Testing

Constraints depict the dependency relationships between parameters in a software system under test. Because almost all systems are constrained in some way, techniques that adequately cater for constraints have become a crucial factor for adoption, deployment and exploitation of Combinatorial Testing (CT). Currently, despite a variety of different constraint handling techniques available, the relationship between these techniques and the generation algorithms that use them remains unknown, yielding an important gap and pressing concern in the literature of constrained combination testing. In this paper, we present a comparative empirical study to investigate the impact of four common constraint handling techniques on the performance of six representative (greedy and search-based) test suite generation algorithms. The results reveal that the Verify technique implemented with the Minimal Forbidden Tuple (MFT) approach is the fastest, while the Replace technique is promising for producing the smallest constrained covering arrays, especially for algorithms that construct test cases one-at-a-time. The results also show that there is an interplay between effectiveness of the constraint handler and the test suite generation algorithm into which it is developed

Enumerator: an efficient approach for enumerating all valid t-tuples

In this paper, we present an efficient approach for enumerating all valid t-tuples for a given configuration space model, which is an important task in computing covering arrays. The results of our experiments suggest that the proposed approach scales better than existing approaches

Building High Strength Mixed Covering Arrays with Constraints

Covering arrays have become a key piece in Combinatorial Testing. In particular, we focus on the efficient construction of Covering Arrays with Constraints of high strength. SAT solving technology has been proven to be well suited when solving Covering Arrays with Constraints. However, the size of the SAT reformulations rapidly grows up with higher strengths. To this end, we present a new incomplete algorithm that mitigates substantially memory blow-ups. The experimental results confirm the goodness of the approach, opening avenues for new practical applications

Experimental Design in Game Testing

The gaming industry has been on constant rise over the last few years. Companies invest huge amounts of money for the release of their games. A part of this money is invested in testing the games. Current game testing methods include manual execution of pre-written test cases in the game. Each test case may or may not result in a bug. In a game, a bug is said to occur when the game does not behave according to its intended design. The process of writing the test cases to test games requires standardization. We believe that this standardization can be achieved by implementing experimental design to video game testing. In this thesis, we discuss the implementation of combinatorial testing to test games. Combinatorial testing is a method of experimental design that is used to generate test cases and is primarily used for commercial software testing. In addition to the discussion of the implementation of combinatorial testing techniques in video game testing, we present a method for finding combinations resulting in video game bugs

An orchestrated survey of available algorithms and tools for Combinatorial Testing

For functional testing based on the input domain of a functionality, parameters and their values are identified and a test suite is generated using a criterion exercising combinations of those parameters and values. Since software systems are large, resulting in large numbers of parameters and values, a technique based on combinatorics called Combinatorial Testing (CT) is used to automate the process of creating those combinations. CT is typically performed with the help of combinatorial objects called Covering Arrays. The goal of the present work is to determine available algorithms/tools for generating a combinatorial test suite. We tried to be as complete as possible by using a precise protocol for selecting papers describing those algorithms/tools. The 75 algorithms/tools we identified are then categorized on the basis of different comparison criteria, including: the test suite generation technique, the support for selection (combination) criteria, mixed covering array, the strength of coverage, and the support for constraints between parameters. Results can be of interest to researchers or software companies who are looking for a CT algorithm/tool suitable for their needs