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Abstract

A bug signature is a set of program elements highlighting the cause or effect of a bug, and provides contextual information for debugging. In order to mine a signature for a buggy program, two sets of execution profiles of the program, one capturing the correct execution and the other capturing the faulty, are examined to identify the program elements contrasting faulty from correct. Signatures solely consisting of control flow transitions have been investigated via discriminative sequence and graph mining algorithms. These signatures might be handicapped in cases where the effect of a bug is not manifested by any deviation in control flow transitions. In this paper, we introduce the notion of predicated bug signature that aims to enhance the predictive power of bug signatures by utilizing both data predicates and control-flow information. We introduce a novel "discriminative itemset generator" mining technique to generate succinct signatures which do not contain redundant or irrelevant program elements. Our case studies demonstrate that predicated signatures can hint at more scenarios of bugs where traditional control-flow signatures fail.

Specification mining is a dynamic analysis process aimed at automatically inferring suggested specifications of a program from its execution traces. We describe a novel method, framework, and tool, for mining inter-object scenario-based specifications in the form of a UML2-compliant variant of Damm and Harel's Live Sequence Charts (LSC). LSC extends the classical partial order semantics of sequence diagrams with temporal liveness and symbolic class level lifelines, in order to generate compact and expressive specifications. The output of our algorithm is a sound and complete set of statistically significant LSCs (i.e., satisfying given thresholds of support and confidence), mined from an input execution trace. We locate statistically significant LSCs by exploring the search space of possible LSCs and checking for their statistical significance. In addition, we use an effective search space pruning strategy, specifically adapted to LSCs, which enables efficient mining of scenarios of arbitrary size. We demonstrate and evaluate the utility of our work in mining informative specifications using a case study on Jeti, a popular, full featured messaging application.

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