Coverage-based approach for model-based testing in software product line

Rapid Quality assurance is an important element in software testing in order to produce high quality products in Software Product Line (SPL). One of the testing techniques that can enhance product quality is Model-Based Testing (MBT). Due to MBT effectiveness in terms of reuse and potential to be adapted, this technique has become an efficient approach that is capable to handle SPL requirements. In this paper, the authors present an approach to manage variability and requirements by using Feature Model (FM) and MBT. This paper focuses on modelling the integration towards enhancing product quality and reducing testing effort. Further, the authors considered coverage criteria, including pairwise coverage, all-state coverage, and all-transition coverage, in order to improve the quality of products. For modelling purposes, the authors constructed a mapping model based on variability in FM and behaviour from statecharts. The proposed approach was validated using mobile phone SPL case study

Validation of Constraints Among Configuration Parameters Using Search-Based Combinatorial Interaction Testing

The appeal of highly-configurable software systems lies in their adaptability to users’ needs. Search-based Combinatorial Interaction Testing (CIT) techniques have been specifically developed to drive the systematic testing of such highly-configurable systems. In order to apply these, it is paramount to devise a model of parameter configurations which conforms to the software implementation. This is a non-trivial task. Therefore, we extend traditional search-based CIT by devising 4 new testing policies able to check if the model correctly identifies constraints among the various software parameters. Our experiments show that one of our new policies is able to detect faults both in the model and the software implementation that are missed by the standard approaches

Combinatorial Interaction Testing for Automated Constraint Repair

Highly-configurable software systems can be easily adapted to address user’s needs. Modelling parameter configurations and their relationships can facilitate software reuse. Combinatorial Interaction Testing (CIT) methods are already often used to drive systematic testing of software system configurations. However, a model of the system’s configurations not conforming with respect to its software implementation, must be repaired in order to restore conformance. In this paper we extend CIT by devising a new search-based technique able to repair a model composed of a set of constraints among the various software system’s parameters. Our technique can be used to detect and fix faults both in the model and in the real software system. Experiments for five real-world systems show that our approach can repair on average 37% of conformance faults. Moreover, we also show it can infer parameter constraints in a large real-world software system, hence it can be used for automated creation of CIT models

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

Learning Combinatorial Interaction Test Generation Strategies Using Hyperheuristic Search

The surge of search based software engineering research has been hampered by the need to develop customized search algorithms for different classes of the same problem. For instance, two decades of bespoke Combinatorial Interaction Testing (CIT) algorithm development, our exemplar problem, has left software engineers with a bewildering choice of CIT techniques, each specialized for a particular task. This paper proposes the use of a single hyperheuristic algorithm that learns search strategies across a broad range of problem instances, providing a single generalist approach. We have developed a Hyperheuristic algorithm for CIT, and report experiments that show that our algorithm competes with known best solutions across constrained and unconstrained problems: For all 26 real-world subjects, it equals or outperforms the best result previously reported in the literature. We also present evidence that our algorithm's strong generic performance results from its unsupervised learning. Hyperheuristic search is thus a promising way to relocate CIT design intelligence from human to machine

Cost-effective model-based test case generation and prioritization for software product line

In Software Product Line (SPL), testing is used to manage core assets that comprised variability and commonality in effective ways due to large sizes of products that continue to be developed. SPL testing requires a technique that is capable to manage SPL core assets. Model-based Testing (MBT) is a promising technique that offers automation and reusability in test cases generation. However, there are difficulties to ensure testing in MBT can achieve good test cases generation results based on cost (size of test suite, total execution time) and effectiveness (coverage criteria, fault detection rate) measures. This is due to lack of trade-off between cost and effectiveness in test cases generated in MBT for SPL. This study aims to increase quality of test cases based on cost and effectiveness by using generation and prioritization approaches for MBT in SPL. This study focuses on three parts to enhance quality of test cases. First, test model development based on traceability link. In order to improve test cases quality, this study focused on implementation of hybrid-based and hyper-heuristic based techniques to generate test cases. This is followed by Test Cases Prioritization (TCP) technique that is based on dissimilarity-based technique with string distance. These test cases generation and prioritization approaches are evaluated by using two benchmarks - one test object and one real object. The results are compared with other prominent approaches. The mapping approach showed 10.27% and 32.39% f-measure improvement against existing approach on e-shop object, respectively. For test cases generation using hybrid-based approach, the proposed approach outperformed existing approaches with 11.66% coverage, 17.78% average execution time, and 45.98% average size of test suite on vending machine object. The hyper-heuristic based approach NSGA-II-LHH outperformed other proposed low-level heuristic approaches with 12.00% improvement on coverage, 46.66% average execution time and 42.54% average size of test suite. Furthermore, evaluation of TCP approaches showed fault detection improvement of 21.60%, 10.40% and 12.20% and total execution time improvement of 48.00%, 22.70% and 31.80% in comparison with three existing approaches. The results revealed that proposed model transformations, test cases generation and prioritization approaches significantly improve cost and effectiveness measure in MBT for SPL

Exploiting Similarity Patterns to Build Generic Test Case Templates for Software Product Line Testing

Ph.DDOCTOR OF PHILOSOPH