Model-based integration testing technique using formal finite state behavioral models for component-based software

Many issues and challenges could be identified when considering integration testing of Component-Based Software Systems (CBSS). Consequently, several research have appeared in the literature, aimed at facilitating the integration testing of CBSS. Unfortunately, they suffer from a number of drawbacks and limitations such as difficulty of understanding and describing the behavior of integrated components, lack of effective formalism for test information, difficulty of analyzing and validating the integrated components, and exposing the components implementation by providing semi-formal models. Hence, these problems have made it in effective to test today’s modern complex CBSS. To address these problems, a model-based approach such as Model-Based Testing (MBT) tends to be a suitable mechanism and could be a potential solution to be applied in the context of integration testing of CBSS. Accordingly, this thesis presents a model-based integration testing technique for CBSS. Firstly, a method to extract the formal finite state behavioral models of integrated software components using Mealy machine models was developed. The extracted formal models were used to detect faulty interactions (integration bugs) or compositional problems between integrated components in the system. Based on the experimental results, the proposed method had significant impact in reducing the number of output queries required to extract the formal models of integrated software components and its performance was 50% better compared to the existing methods. Secondly, based on the extracted formal models, an effective model-based integration testing technique (MITT) for CBSS was developed. Finally, the effectiveness of the MITT was demonstrated by employing it in the air gourmet and elevator case studies, using three evaluation parameters. The experimental results showed that the MITT was effective and outperformed Shahbaz technique on the air gourmet and elevator case studies. In terms of learned components for air gourmet and elevator case studies respectively, the MITT results were better by 98.14% and 100%, output queries based on performance were 42.13% and 25.01%, and error detection capabilities were 70.62% and 75% for each of the case study

Mobile app and app store analysis, testing and optimisation

This talk presents results on analysis and testing of mobile apps and app stores, reviewing the work of the UCL App Analysis Group (UCLappA) on App Store Mining and Analysis. The talk also covers the work of the UCL CREST centre on Genetic Improvement, applicable to app improvement and optimisation

Threats to the validity of mutation-based test assessment

Much research on software testing and test techniques relies on experimental studies based on mutation testing. In this paper we reveal that such studies are vulnerable to a potential threat to validity, leading to possible Type I errors; incorrectly rejecting the Null Hypothesis. Our findings indicate that Type I errors occur, for arbitrary experiments that fail to take countermeasures, approximately 62% of the time. Clearly, a Type I error would potentially compromise any scientific conclusion. We show that the problem derives from such studies’ combined use of both subsuming and subsumed mutants. We collected articles published in the last two years at three leading software engineering conferences. Of those that use mutation-based test assessment, we found that 68% are vulnerable to this threat to validity

An Empirical Study on Mutation, Statement and Branch Coverage Fault Revelation that Avoids the Unreliable Clean Program Assumption

Many studies suggest using coverage concepts, such as branch coverage, as the starting point of testing, while others as the most prominent test quality indicator. Yet the relationship between coverage and fault-revelation remains unknown, yielding uncertainty and controversy. Most previous studies rely on the Clean Program Assumption, that a test suite will obtain similar coverage for both faulty and fixed (‘clean’) program versions. This assumption may appear intuitive, especially for bugs that denote small semantic deviations. However, we present evidence that the Clean Program Assumption does not always hold, thereby raising a critical threat to the validity of previous results. We then conducted a study using a robust experimental methodology that avoids this threat to validity, from which our primary finding is that strong mutation testing has the highest fault revelation of four widely-used criteria. Our findings also revealed that fault revelation starts to increase significantly only once relatively high levels of coverage are attained

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