Browsing by Author "LO, David"
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- ItemMining Modal Scenarios from Program Execution Traces(2007-08-30) LO, David; MAOZ, Shahar; KHOO, Siau ChengSpecification 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.
- ItemMining Past-Time Temporal Rules(2008-05-27) LO, David; KHOO, Siau-Cheng; LIU, ChaoSpecification mining is a process of extracting specifications, often from program execution traces. These specifications can in turn be used to aid program understanding, monitoring and verification. There are a number of dynamic-analysis-based specification mining tools in the literature, however none so far extract past time temporal expressions in the form of rules stating: ``whenever a series of events occur, previously another series of events happened before''. Rules of this format are commonly found in practice and useful for various purposes. Most rule-based specification mining tools only mine future-time temporal expression. Many past-time temporal rules like ``whenever a resource is used, it was allocated before'' are asymmetric as the other direction does not holds. Hence, there is a need to mine past-time temporal rules. In this paper, we describe an approach to mine significant rules of the above format occurring above a certain statistical thresholds from program exe...
- ItemSMArTIC: Specification Mining Architecture with Trace fIltering and Clustering(2006-08-31) LO, David; KHOO, Siau-ChengImproper management of software evolution, compounded by imprecise, and changing requirements and short time to market requirement, commonly leads to a lack of up-to-date specification. This can result in software that is characterized by presence of bugs, anomalies and even security threats. Software specification mining is a new technique to address this concern by inferring specifications automatically. In this paper, we propose a novel API specification mining architecture called SMArTIC (Specification Mining Architecture with Trace fIltering and Clustering) to improve the accuracy, robustness and scalability of specification miners. This architecture is constructed based on two hypotheses: (1) Erroneous transactions should be pruned from traces input to a miner, and (2) Clustering related traces will localize inaccuracies in learning and reduce over-generalization. Corresponding, SMArTIC comprises four components: an erroneous-trace filtering block, a related-trace clustering block, a learner, and a merger. We show through experiment that the quality of specification miner can be significantly improved using SMArTIC.