Software Library Usage Pattern Extraction Using a Software Model Checker

Liu, Chang; Ye, En; Richardson, Debra J.

doi:10.1109/ase.2006.63

Cited by 20 publications

(11 citation statements)

References 3 publications

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“…Other static approaches to mine API specifications and then leverage them to detect bugs [16,28,30,31,33]. FindBugs [25] looks for specified bug patterns.…”

Section: Related Workmentioning

confidence: 99%

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Mining preconditions of APIs in large-scale code corpus

Nguyen

Dyer

Nguyen

et al. 2014

Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering

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Modern software relies on existing application programming interfaces (APIs) from libraries. Formal specifications for the APIs enable many software engineering tasks as well as help developers correctly use them. In this work, we mine large-scale repositories of existing open-source software to derive potential preconditions for API methods. Our key idea is that APIs' preconditions would appear frequently in an ultra-large code corpus with a large number of API usages, while project-specific conditions will occur less frequently. First, we find all client methods invoking APIs. We then compute a control dependence relation from each call site and mine the potential conditions used to reach those call sites. We use these guard conditions as a starting point to automatically infer the preconditions for each API. We analyzed almost 120 million lines of code from SourceForge and Apache projects to infer preconditions for the standard Java Development Kit (JDK) library. The results show that our technique can achieve high accuracy with recall from 75-80% and precision from 82-84%. We also found 5 preconditions missing from human written specifications. They were all confirmed by a specification expert. In a user study, participants found 82% of the mined preconditions as a good starting point for writing specifications. Using our mining result, we also built a benchmark of more than 4,000 precondition-related bugs.

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“…Other static approaches to mine API specifications and then leverage them to detect bugs [16,28,30,31,33]. FindBugs [25] looks for specified bug patterns.…”

Section: Related Workmentioning

confidence: 99%

“…There are several dynamic approaches in mining specifications [5,12,13,17,21,33,35,43,53,58]. Daikon [17] automatically detects invariants in a program via running test cases.…”

Section: Related Workmentioning

confidence: 99%

Mining preconditions of APIs in large-scale code corpus

Nguyen

Dyer

Nguyen

et al. 2014

Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering

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“…Mining-based methods are proposed [2,3,7,27,28,33,35,36,38,50,65,68] to discover API usage rules from executing traces or source codes, where API usage rules are represented by some patterns or finite automata. One can apply model-checking techniques to check whether programs violate or not API usage rules represented by patterns or finite automata.…”

Section: Related Workmentioning

confidence: 99%

“…Many works addressed this problem [7,25,27,28,31,33,[35][36][37][38]43,50,64,65,68]. However, their approaches either cannot formally describe API usage rules in a precise way or cannot automatically check API usage rules.…”

Section: Introductionmentioning

confidence: 97%

Model-checking software library API usage rules

Song

Touili

2015

Softw Syst Model

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Modern software increasingly relies on using third-party libraries which are accessed via application programming interfaces (APIs). Libraries usually impose constraints on how API functions can be used (API usage rules) and programmers have to obey these API usage rules. However, API usage rules often are not well documented or documented informally. In this work, we show how to use the SCTPL and SLTPL logics to precisely and formally specify API usage rules in libraries, where SCTPL/SLTPL can be seen as an extension of the branching/linear temporal logic CTL/LTL with variables, quantifiers and predicates over the stack. This allows library providers to formally describe API usage rules without knowing how their libraries will be used by programmers. We propose an automated approach to check whether programs using libraries violate API usage rules or not. Our approach consists in modeling programs as pushdown systems (PDSs) and checking API usage rules by SCTPL/SLTPL model-checking for PDSs. To make the model-checking procedure more efficient and precise, we propose an abstraction that reduces drastically the size of the program model and integrate may-alias analysis into our approach to reduce false alarms. Moreover, we characterize two sublogics rSCTPL and rSLTPL of SCTPL and Communicated by SLTPL that are preserved by the abstraction. We implement our techniques in a tool and apply the tool to check several open-source programs. Our tool finds several previously unknown bugs in several programs. The may-alias analysis avoids most of the false alarms that occur using SCTPL or SLTPL model-checking techniques without may-alias analysis.

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“…2001);Kremenek et al (2006b);Li et al (2006);Li and Zhou (2005);Liu et al (2006). FindBugs Hovemeyer and Pugh (2004) looks for specified bug patterns.…”

mentioning

confidence: 99%

Analyzing repetitiveness in big code to support software maintenance and evolution

Nguyen

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Software Library Usage Pattern Extraction Using a Software Model Checker

Cited by 20 publications

References 3 publications

Mining preconditions of APIs in large-scale code corpus

Mining preconditions of APIs in large-scale code corpus

Model-checking software library API usage rules

Analyzing repetitiveness in big code to support software maintenance and evolution

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