Controllability problems in MSC-based testing

This is a pre-copyedited, author-produced PDF of an article accepted for publication in The Computer Journal following peer review. The definitive publisher-authenticated version [Dan, H and Hierons, RM (2012), "Controllability Problems in MSC-Based Testing", The Computer Journal, 55(11), 1270-1287] is available online at: http://comjnl.oxfordjournals.org/content/55/11/1270. Copyright @ The Authors 2011.In testing systems with distributed interfaces/ports, we may place a separate tester at each port. It is known that this approach can introduce controllability problems which have received much attention in testing from finite state machines. Message sequence charts (MSCs) form an alternative, commonly used, language for modelling distributed systems. However, controllability problems in testing from MSCs have not been thoroughly investigated. In this paper, controllability problems in MSC test cases are analysed with three notions of observability: local, tester and global. We identify two types of controllability problem in MSC-based testing. It transpires that each type of controllability problem is related to a type of MSC pathology. Controllability problems of timing are caused by races but not every race causes controllability problems; controllability problems of choice are caused by non-local choices and not every non-local choice causes controllability problems. We show that some controllability problems of timing are avoidable and some controllability problems of choice can be overcome when testers have better observational power. Algorithms are provided to tackle both types of controllability problems. Finally, we show how one can overcome controllability problems using a coordination service with status messages based on algorithms developed in this paper.EPSR

Higher Order Mutation Testing

Mutation testing is a fault-based software testing technique that has been studied widely for over three decades. To date, work in this field has focused largely on first order mutants because it is believed that higher order mutation testing is too computationally expensive to be practical. This thesis argues that some higher order mutants are potentially better able to simulate real world faults and to reveal insights into programming bugs than the restricted class of first order mutants. This thesis proposes a higher order mutation testing paradigm which combines valuable higher order mutants and non-trivial first order mutants together for mutation testing. To overcome the exponential increase in the number of higher order mutants a search process that seeks fit mutants (both first and higher order) from the space of all possible mutants is proposed. A fault-based higher order mutant classification scheme is introduced. Based on different types of fault interactions, this approach classifies higher order mutants into four categories: expected, worsening, fault masking and fault shifting. A search-based approach is then proposed for locating subsuming and strongly subsuming higher order mutants. These mutants are a subset of fault mask and fault shift classes of higher order mutants that are more difficult to kill than their constituent first order mutants. Finally, a hybrid test data generation approach is introduced, which combines the dynamic symbolic execution and search based software testing approaches to generate strongly adequate test data to kill first and higher order mutants

Choice and chance:model-based testing of stochastic behaviour

Probability plays an important role in many computer applications. A vast number of algorithms, protocols and computation methods uses randomisation to achieve their goals. A crucial question then becomes whether such probabilistic systems work as intended. To investigate this, such systems are often subjected to a large number of well-designed test cases, that compare a observed behaviour to a requirements specification. Model-based testing is an innovative testing technique rooted in formal methods, that aims at automating this labour intense and often error-prone manual task. By providing faster and more thorough testing at lower cost, it has gained rapid popularity in industry and academia alike. However, classic model-based testing methods are insufficient when dealing with inherently stochastic systems. This thesis introduces a rigorous model-based testing framework, that is capable to automatically test such systems. The presented methods are capable of judging functional correctness, discrete probability choices, and hard and soft-real time constraints. The framework is constructed in a clear step-by-step approach. First, the model-based testing landscape is laid out, and related work is discussed. Next, we instantiate a model-based testing framework to highlight the purpose of individual theoretical components like, e.g., a conformance relation, test cases, and practical test generation algorithms. This framework is then conservatively extended by introducing discrete probability choices to the specification language. A last step further extends this probabilistic framework by adding hard and soft real time constraints. Classical functional correctness verdicts are thus extended with goodness of fit methods known from statistics. Proofs of the framework’s correctness are presented before its capabilities are exemplified by studying smaller scale case studies known from the literature. The framework reconciles non-deterministic and probabilistic choices in a fully-fledged way via the use of schedulers. Schedulers then become a subject worthy to study in their own rights. This is done in the second part of this thesis; we introduce a most natural equivalence relation based on schedulers for Markov automata, and compare its distinguishing power to notions of trace distributions and bisimulation relations. Lastly, the power of different scheduler classes of stochastic automata is investigated. We compare reachability probabilities of different schedulers by altering the information available to them. A hierarchy of scheduler classes is established, with the intent to reduce complexity of related problems by gaining near optimal results for smaller scheduler classes