Graph-based mining of multiple object usage patterns

Nguyen, Tung Thanh; Nguyen, Hoan Anh; Pham, N.; Al-Kofahi, Jafar M.; Nguyen, Tien N.

doi:10.1145/1595696.1595767

Cited by 244 publications

(179 citation statements)

References 36 publications

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“…To reduce manual inspection time, we first gathered similar patches using groums [16]. A groum is a graph-based model for representing object usage.…”

Section: B Mining Common Patchesmentioning

confidence: 99%

Automatic patch generation learned from human-written patches

Kim

Nam

Song

et al. 2013

2013 35th International Conference on Software Engineering (ICSE)

453

612

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Abstract-Patch generation is an essential software maintenance task because most software systems inevitably have bugs that need to be fixed. Unfortunately, human resources are often insufficient to fix all reported and known bugs. To address this issue, several automated patch generation techniques have been proposed. In particular, a genetic-programming-based patch generation technique, GenProg, proposed by Weimer et al., has shown promising results. However, these techniques can generate nonsensical patches due to the randomness of their mutation operations.To address this limitation, we propose a novel patch generation approach, Pattern-based Automatic program Repair (PAR), using fix patterns learned from existing human-written patches. We manually inspected more than 60,000 human-written patches and found there are several common fix patterns. Our approach leverages these fix patterns to generate program patches automatically.

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“…To reduce manual inspection time, we first gathered similar patches using groums [16]. A groum is a graph-based model for representing object usage.…”

Section: B Mining Common Patchesmentioning

confidence: 99%

Automatic patch generation learned from human-written patches

Kim

Nam

Song

et al. 2013

2013 35th International Conference on Software Engineering (ICSE)

453

612

View full text Add to dashboard Cite

show abstract

“…A mining partial orders technique [6] enables multiple possible paths of invocations to be captured in a frequent partial order. GrouMiner [7] transforms source code into graph representations and returns frequent sub-graphs as usage patterns.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, mining method invocation sequences from repositories is significant and is expected to be feasible. In the recent research trends, several mining techniques are applied into repositories to extract usage patterns, such as association rules [2], [3], frequent itemsets [4], frequent subsequences [3], [5], frequent partial orders [6], and frequent sub-graphs mining [7]. These techniques can produce several usage patterns.…”

Section: Introductionmentioning

confidence: 99%

Mining API Usage Patterns by Applying Method Categorization to Improve Code Completion

Akbar

Omori

Maruyama

2014

IEICE Trans. Inf. & Syst.

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SUMMARYDevelopers often face difficulties while using APIs. API usage patterns can aid them in using APIs efficiently, which are extracted from source code stored in software repositories. Previous approaches have mined repositories to extract API usage patterns by simply applying data mining techniques to the collection of method invocations of API objects. In these approaches, respective functional roles of invoked methods within API objects are ignored. The functional role represents what type of purpose each method actually achieves, and a method has a specific predefined order of invocation in accordance with its role. Therefore, the simple application of conventional mining techniques fails to produce API usage patterns that are helpful for code completion. This paper proposes an improved approach that extracts API usage patterns at a higher abstraction level rather than directly mining the actual method invocations. It embraces a multilevel sequential mining technique and uses categorization of method invocations based on their functional roles. We have implemented a mining tool and an extended Eclipse's code completion facility with extracted API usage patterns. Evaluation results of this tool show that our approach improves existing code completion.

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“…Another popular field is mining frequent call sequences from an API [10], [1], [16]. The goal of this is to use other projects to predict the sequence of method calls the developer wishes to write.…”

Section: Related Workmentioning

confidence: 99%

Overthrowing the Tyranny of Alphabetical Ordering in Documentation Systems

Spasojevic

Lungu

Nierstrasz

2014

2014 IEEE International Conference on Software Maintenance and Evolution

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Abstract-Software developers are often unsure of the exact name of the API method they need to use to invoke the desired behavior. Most state-of-the-art documentation browsers present API artefacts in alphabetical order. Albeit easy to implement, alphabetical order does not help much: if the developer knew the name of the required method, he could have just searched for it in the first place.In a context where multiple projects use the same API, and their source code is available, we can improve the API presentation by organizing the elements in the order in which they are more likely to be used by the developer. Usage frequency data for methods is gathered by analyzing other projects from the same ecosystem and this data is used then to improve tools.We present a preliminary study on the potential of this approach to improve the API presentation by reducing the time it takes to find the method that implements a given feature. We also briefly present our experience with two proof-of-concept tools implemented for Smalltalk and Java.

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Graph-based mining of multiple object usage patterns

Cited by 244 publications

References 36 publications

Automatic patch generation learned from human-written patches

Automatic patch generation learned from human-written patches

Mining API Usage Patterns by Applying Method Categorization to Improve Code Completion

Overthrowing the Tyranny of Alphabetical Ordering in Documentation Systems

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