Automatic identification of key classes in a software system using webmining techniques
Journal of Software Maintenance and Evolution: Research and Practice
Dynamic decision tree for legacy use-case recovery
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Proceedings of the 2013 Research in Adaptive and Convergent Systems
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Understanding a large execution trace is not easy task due to the size and complexity of typical traces. In this thesis, we present various techniques that tackle this problem. Firstly, we present a set of metrics for measuring various properties of an execution trace in order to assess the work required for understanding its content. We show the result of applying these metrics to thirty traces generated from three different software systems. We discuss how these metrics can be supported by tools to facilitate the exploration of traces based on their complexity. Secondly, we present a novel technique for manipulating traces called trace summarization, which consists of taking a trace as input and return a summary of its main content as output. Traces summaries can be used to enable top-down analysis of traces as well as the recovery of the system behavioural models. In this thesis, we present a trace summarization algorithm that is based on successive filtering of implementation details from traces. An analysis of the concept of implementation details such as utilities is also presented. Thirdly, we have developed a scalable exchange format called the Compact Trace Format (CTF) in order to enable sharing and reusing of traces. The design of CTF satisfies well-known requirements for a standard exchange format. Finally, this thesis includes a survey of eight trace analysis tools. A study of the advantages and limitations of the techniques supported by these tools is provided. The approaches presented in this thesis have been applied to real software systems. The obtained results demonstrate the effectiveness and usefulness of our techniques.