Abstract-This paper is concerned with the challenge of reorganising a software system into modules that both obey sound design principles and are sensible to domain experts. The problem has given rise to several unsupervised automated approaches that use techniques such as clustering and Formal Concept Analysis. Although results are often partially correct, they usually require refinement to enable the developer to integrate domain knowledge. This paper presents the SUMO algorithm, an approach that is complementary to existing techniques and enables the maintainer to refine their results. The algorithm is guaranteed to eventually yield a result that is satisfactory to the maintainer, and the evaluation on a diverse range of systems shows that this occurs with a reasonably low amount of effort.

Where a software component is updated or replaced regression testing is required. Regression test sets can contain considerable redundancy. This is especially true in the case where no formal regression test set exists and the new component must instead be compared against patterns of behaviour derived from in-use log data from the previous version. Previous work has applied search-based techniques such as Genetic Algorithms to minimise test sets, but these relied on code coverage metrics to select test cases. Recent work has demonstrated the advantage of behaviour inference as a test adequacy metric. This paper presents a multi-objective search-based technique that uses behaviour inference as the fitness metric. The resulting test sets are evaluated using mutation testing and it is demonstrated that a considerably reduced test set can be found that retains all of the fault finding capability of the complete set.

Introduction: Pyomyositis, or isolated abscess formation within a skeletal muscle, is a relatively common condition in tropical climates but it is only encountered rarely in temperate zones.

Abstract-Current software remodularisation tools only operate on abstractions of a software system. In this paper, we investigate the actual impact of automated remodularisation on source code using a tool that automatically applies remodularisations as refactorings. This shows us that a typical remodularisation (as computed by the Bunch tool) will require changes to thousands of lines of code, spread throughout the system (typically no code files remain untouched). In a typical multi-developer project this presents a serious integration challenge, and could contribute to the low uptake of such tools in an industrial context. We relate these findings with our ongoing research into techniques that produce iterative commit friendly code changes to address this problem.

Abstract-The challenge of inferring state machines from log data or execution traces is well-established, and has led to the development of several powerful techniques. Current approaches tend to focus on the inference of conventional finite state machines or, in few cases, state machines with guards. However, these machines are ultimately only partial, because they fail to model how any underlying variables are computed during the course of an execution; they are not computational. In this paper we introduce a technique based upon Genetic Programming to infer these data transformation functions, which in turn render inferred automata fully computational. Instead of merely determining whether or not a sequence is possible, they can be simulated, and be used to compute the variable values throughout the course of an execution. We demonstrate the approach by using a CrossValidation study to reverse-engineer complete (computational) EFSMs from traces of established implementations.