Efficient mining of iterative patterns for software specification discovery

Lo, David; Khoo, Siau‐Cheng; Liu, Chao

doi:10.1145/1281192.1281243

Cited by 96 publications

(59 citation statements)

References 31 publications

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“…There are many standard verification tools readily taking software properties expressed in LTL as inputs. Since the underlying target formalisms and semantics are different, both the search space pruning strategies and the mining algorithm are very different from our previous work in [20].…”

Section: Related Workmentioning

confidence: 85%

“…A comparative study to compare recurrent rules with other forms of software specifications mined from execution traces [3,19,20] is another future work. Improving scalability of the mining process further and extending to mining from stream data are other possible future work.…”

Section: Discussionmentioning

confidence: 99%

“…In [20], we proposed iterative patterns to discover software specifications, which are defined based on the semantics of Message Sequence Charts (MSC) [15]. Different from [20], this work is based on a different formalism, namely the semantics of Linear Temporal Logic (LTL).…”

Section: Related Workmentioning

confidence: 99%

“…Different from [20], this work is based on a different formalism, namely the semantics of Linear Temporal Logic (LTL). LTL has a wider application area than MSC, ranging from software engineering [29] to security & privacy [4].…”

Section: Related Workmentioning

confidence: 99%

“…Mining for knowledge from data has been shown useful for many purposes [12] ranging from finance, advertising, bio-informatics and recently software engineering [17,20]. Addressing the same issue of knowledge discovery from data, we study the problem of mining recurrent rules, each having the following form:…”

Section: Introductionmentioning

confidence: 99%

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Efficient Mining of Recurrent Rules from a Sequence Database

Khoo

Liu

Database Systems for Advanced Applications

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Abstract. We study a novel problem of mining significant recurrent rules from a sequence database. Recurrent rules have the form "whenever a series of precedent events occurs, eventually a series of consequent events occurs". Recurrent rules are intuitive and characterize behaviors in many domains. An example is in the domain of software specifications, in which the rules capture a family of program properties beneficial to program verification and bug detection. Recurrent rules generalize existing work on sequential and episode rules by considering repeated occurrences of premise and consequent events within a sequence and across multiple sequences, and by removing the "window" barrier. Bridging the gap between mined rules and program specifications, we formalize our rules in linear temporal logic. We introduce and apply a novel notion of rule redundancy to ensure efficient mining of a compact representative set of rules. Performance studies on benchmark datasets and a case study on an industrial system have been performed to show the scalability and utility of our approach.

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Section: Related Workmentioning

confidence: 85%

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient Mining of Recurrent Rules from a Sequence Database

Khoo

Liu

Database Systems for Advanced Applications

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SCAN: an approach to label and relate execution trace segments

Medini

Arnaoudova

Penta

et al. 2014

J Software Evolu Process

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Abstract-Identifying concepts in execution traces is a task often necessary to support program comprehension or maintenance activities. Several approaches-static, dynamic or hybrid-have been proposed to identify cohesive, meaningful sequence of methods in execution traces. However, none of the proposed approaches is able to label such segments and to identify relations between segments of the same trace.This paper present SCAN (Segment Concept AssigNer) an approach to assign labels to sequences of methods in execution traces, and to identify relations between such segments. SCAN uses information retrieval methods and formal concept analysis to produce sets of words helping the developer to understand the concept implemented by a segment. Specifically, formal concept analysis allows SCAN to discover commonalities between segments in different trace areas, as well as terms more specific to a given segment and high level relations between segments.The paper describes SCAN along with a preliminary manual validation-upon execution traces collected from usage scenarios of JHotDraw and ArgoUML-of SCAN accuracy in assigning labels representative of concepts implemented by trace segments.

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Mining temporal rules for software maintenance

Lo¹,

Khoo²,

Liu³

2008

J. Softw. Maint. Evol.: Res. Pract.

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Software evolution incurs difficulties in program comprehension and software verification, and hence it increases the cost of software maintenance. In this study, we propose a novel technique to mine from program execution traces a sound and complete set of statistically significant temporal rules of arbitrary lengths. The extracted temporal rules reveal invariants that the program observes, and will consequently guide developers to understand the program behaviors, and facilitate all downstream applications such as verification and debugging. Different from previous studies that were restricted to mining two‐event rules (e.g., 〈lock〉→〈unlock〉), our algorithm discovers rules of arbitrary lengths. In order to facilitate downstream applications, we represent the mined rules as temporal logic expressions, so that existing model checkers or other formal analysis toolkit can readily consume our mining results. Performance studies on benchmark data sets and a case study on an industrial system have been performed to show the scalability and utility of our approach. We performed case studies on JBoss application server and a buggy concurrent versions system application, and the result clearly demonstrates the usefulness of our technique in recovering underlying program designs and detecting bugs. Copyright © 2008 John Wiley & Sons, Ltd.

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Efficient mining of iterative patterns for software specification discovery

Cited by 96 publications

References 31 publications

Efficient Mining of Recurrent Rules from a Sequence Database

Efficient Mining of Recurrent Rules from a Sequence Database

SCAN: an approach to label and relate execution trace segments

Mining temporal rules for software maintenance

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