On the Use of Stemming for Concern Location and Bug Localization in Java

Hill, Emily; Rao, Shivani; Kak, Avinash C.

doi:10.1109/scam.2012.29

Cited by 31 publications

(21 citation statements)

References 33 publications

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“…They also claim that the accuracy of TR techniques is highly dependent on the configuration of the tool. Their claim is supported by recent research (e.g., [Biggers, Bocovich, Capshaw, Eddy, Etzkorn, and Kraft, 2012;Dit, Guerrouj, Poshyvanyk, and Antoniol, 2011;Hill, Rao, and Kak, 2012]). …”

Section: Overviewmentioning

confidence: 63%

Structural information based term weighting in text retrieval for feature location

Bassett

Kraft

2013

2013 21st International Conference on Program Comprehension (ICPC)

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Feature location is a program comprehension activity in which a developer identifies source code entities that implement a feature of interest. Recent feature location techniques apply text retrieval techniques to corpora built from text embedded in source code. These techniques are highly configurable, but many of the available parameters remain unexplored in the software engineering context. For example, while the natural language processing community has developed several term weighting schemes meant to highlight the importance of certain terms in a particular document, the software engineering community has thus far not developed new term weighting schemes for use with source code. Thus, we propose a new term weighting scheme that is based on the structural information in source code. We then report the results of an empirical study in which we evaluated the performance effects of the proposed term weighting scheme on a latent Dirichlet allocation (LDA) based feature location technique (FLT). In all, we studied over 400 bugs and features from five open source Java systems. Our key finding is that the accuracy of the LDA-based FLT improves when a structural term weighting scheme is used rather than a uniform term weighting scheme.ii ACKNOWLEDGMENTS

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Section: Overviewmentioning

confidence: 63%

Structural information based term weighting in text retrieval for feature location

Bassett

Kraft

2013

2013 21st International Conference on Program Comprehension (ICPC)

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“…We observe no significant difference among the three methods. In prior work, Hill et al [13] also observed that no single stemmer is better for all kinds of queries. While we choose Krovetz somewhat arbitrarily, as the more conservative of the two stemming algorithms, closer analysis here appears to be warranted to provide a fuller explanation.…”

Section: System Tuningmentioning

confidence: 94%

Improving bug localization using structured information retrieval

Saha

Lease

Khurshid

et al. 2013

2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE)

309

359

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Abstract-Locating bugs is important, difficult, and expensive, particularly for large-scale systems. To address this, natural language information retrieval techniques are increasingly being used to suggest potential faulty source files given bug reports. While these techniques are very scalable, in practice their effectiveness remains low in accurately localizing bugs to a small number of files. Our key insight is that structured information retrieval based on code constructs, such as class and method names, enables more accurate bug localization. We present BLUiR, which embodies this insight, requires only the source code and bug reports, and takes advantage of bug similarity data if available. We build BLUiR on a proven, open source IR toolkit that anyone can use. Our work provides a thorough grounding of IR-based bug localization research in fundamental IR theoretical and empirical knowledge and practice. We evaluate BLUiR on four open source projects with approximately 3,400 bugs. Results show that BLUiR matches or outperforms a current state-of-theart tool across applications considered, even when BLUiR does not use bug similarity data used by the other tool.

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“…Emily Hill et al [78] have conducted qualitative study of stemmers on software domain, source code, specifically on java. To check the impact of stemming and retrieval effectiveness, they used Mean Average Precision (MAP) and Rank measure and conducted quantitative study of query-byquery.…”

Section: Content Analysismentioning

confidence: 99%

An Overview on User Profiling in Online Social Networks

Vasanthakumar¹,

Sunithamma²,

Shenoy³

et al. 2017

IJAIS

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Advances in Online Social Networks is creating huge data day in and out providing lot of opportunities to its users to express their interest and opinion. Due to the popularity and exposure of social networks, many intruders are using this platform for illegal purposes. Identifying such users is challenging and requires digging huge knowledge out of the data being flown in the social media. This work gives an insight to profile users in online social networks. User Profiles are established based on the behavioral patterns, correlations and activities of the user analyzed from the aggregated data using techniques like clustering, behavioral analysis, content analysis and face detection. Depending on application and purpose, the mechanism used in profiling users varies. Further study on other mechanisms used in profiling users is under the scope of future endeavors.

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On the Use of Stemming for Concern Location and Bug Localization in Java

Cited by 31 publications

References 33 publications

Structural information based term weighting in text retrieval for feature location

Structural information based term weighting in text retrieval for feature location

Improving bug localization using structured information retrieval

An Overview on User Profiling in Online Social Networks

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