Surfacing code in the dark: an instant clone search approach

Park, Jin-Woo; Lee, Mu-Woong; Roh, Jong-Won; Hwang, Seung-won; Kim, Sunghun

doi:10.1007/s10115-013-0677-z

Cited by 9 publications

(13 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Higo et al [11] observe that new programming libraries can be identified using clone detection across a large number of software systems. Furthermore, Type-3 clone detection can be used to intelligently tag code fragments [28], or recommend code examples [19].…”

Section: Introductionmentioning

confidence: 99%

Threshold-free code clone detection for a large-scale heterogeneous Java repository

Keivanloo

Zhang

Zou

2015

2015 IEEE 22nd International Conference on Software Analysis, Evolution, and Reengineering (SANER)

View full text Add to dashboard Cite

Code clones are unavoidable entities in software ecosystems. A variety of clone-detection algorithms are available for finding code clones. For Type-3 clone detection at method granularity (i.e., similar methods with changes in statements), dissimilarity threshold is one of the possible configuration parameters. Existing approaches use a single threshold to detect Type-3 clones across a repository. However, our study shows that to detect Type-3 clones at method granularity on a large-scale heterogeneous repository, multiple thresholds are often required. We find that the performance of clone detection improves if selecting different thresholds for various groups of clones in a heterogeneous repository (i.e., various applications). In this paper, we propose a threshold-free approach to detect Type-3 clones at method granularity across a large number of applications. Our approach uses an unsupervised learning algorithm, i.e., k-means, to determine true and false clones. We use a clone benchmark with 330,840 tagged clones from 24,824 open source Java projects for our study. We observe that our approach improves the performance significantly by 12% in terms of Fmeasure. Furthermore, our threshold-free approach eliminates the concern of practitioners about possible misconfiguration of Type-3 clone detection tools.

show abstract

Section: Introductionmentioning

confidence: 99%

Threshold-free code clone detection for a large-scale heterogeneous Java repository

Keivanloo

Zhang

Zou

2015

2015 IEEE 22nd International Conference on Software Analysis, Evolution, and Reengineering (SANER)

View full text Add to dashboard Cite

show abstract

“…We selected Stack Overflow because it is one of the most popular programming Q&A websites available with approximately 7.6 million users. There are a large number of code snippets reused from the site [3], and there are also several studies encouraging of doing so (e.g., [33], [49], [52], [53]). Nonetheless, it may not be representative to all the programming Q&A websites.…”

Section: Internal Validitymentioning

confidence: 99%

“…It allows programmers to ask questions and give answers to programming problems. The website has found to be useful for software development [16], [33], [49], [52], [53], [69], [70], [74] and also valuable for educational purposes [47]. On Stack Overflow, each conversation contains a question and a list of answers.…”

Section: Introductionmentioning

confidence: 99%

“…Stack Overflow answerers frequently clone code from other locations, such as their personal projects, company projects, and open source projects, to Stack Overflow as a solution or a complement to a solution. The code cloning activity on Stack Overflow is obviously beneficial considered the popularity of Stack Overflow and its influence on software development [49], [52], [53]. On the other hand, there is also a downside caused by low quality, defective, and harmful code snippets that are reused without awareness by millions of users [2], [20], [83].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Toxic Code Snippets on Stack Overflow

Ragkhitwetsagul

Krinke

Paixao

et al. 2021

IIEEE Trans. Software Eng.

View full text Add to dashboard Cite

Online code clones are code fragments that are copied from software projects or online sources to Stack Overflow as examples. Due to an absence of a checking mechanism after the code has been copied to Stack Overflow, they can become toxic code snippets, e.g., they suffer from being outdated or violating the original software license. We present a study of online code clones on Stack Overflow and their toxicity by incorporating two developer surveys and a large-scale code clone detection. A survey of 201 high-reputation Stack Overflow answerers (33% response rate) showed that 131 participants (65%) have ever been notified of outdated code and 26 of them (20%) rarely or never fix the code. 138 answerers (69%) never check for licensing conflicts between their copied code snippets and Stack Overflow's CC BY-SA 3.0. A survey of 87 Stack Overflow visitors shows that they experienced several issues from Stack Overflow answers: mismatched solutions, outdated solutions, incorrect solutions, and buggy code. 85% of them are not aware of CC BY-SA 3.0 license enforced by Stack Overflow, and 66% never check for license conflicts when reusing code snippets. Our clone detection found online clone pairs between 72,365 Java code snippets on Stack Overflow and 111 open source projects in the curated Qualitas corpus. We analysed 2,289 non-trivial online clone candidates. Our investigation revealed strong evidence that 153 clones have been copied from a Qualitas project to Stack Overflow. We found 100 of them (66%) to be outdated, of which 10 were buggy and harmful for reuse. Furthermore, we found 214 code snippets that could potentially violate the license of their original software and appear 7,112 times in 2,427 GitHub projects.We performed an empirical study of online code clones between Stack Overflow and 111 Java open source projects 8. https://ucl-crest.github.io/cloverflow-web This is the author's version of an article that has been published in this journal. Changes were made to this version by the publisher prior to publication.The final version of record is available at http://dx.

show abstract

“…We use recommender systems to model estimated cost automatically. Second, CosTriage+ considers code similarity [22][23][24][25][26] as additional signals. This can not only complement existing dominant text-based approach, by handling non-topic cases, but also significantly widens applicability to automatically reported bugs without proper reports (crash) or mixed-lingual bug reporting environments.…”

Section: Related Workmentioning

confidence: 99%

Cost-aware triage ranking algorithms for bug reporting systems

et al. 2015

Self Cite

View full text Add to dashboard Cite

Bug triaging of deciding whom to fix the bug has been studied actively. However, existing work does not consider varying cost of the same bug over developers with diverse backgrounds and experiences. In clear contrast, we argue the "cost" of one bug can be low for one developer, while high for another. Based on this view, we study an automatic triaging system considering both accuracy and cost. Our preliminary solution, CosTriage, models user-specific experiences and estimated cost on each bug category, obtained from topic modeling, and assigns the bug to the developer who not only can, but also is expected to fix fast. For user-specific cost modeling, we are inspired by recommender system work, of estimating user-specific rating of items, e.g., movies. With this view, existing triaging work of categorizing bugs and assigning developers with experiences in the category falls into contentbased recommendation (CBR). However, CBR is well known to cause overspecialization because it recommends only the types of bugs that each developer has solved before. This problem is critical because the experienced developers can become overloaded with bugs they hate to fix, though there exist other categories he can fix faster. CosTriage adopts contentThis work builds on and extends our preliminary work [1]. B Seung-won Hwang 123 J. Park et al.boosted collaborative filtering (CBCF), considering not only similar bugs (content-based) but similar developers (collaborative) for estimating user-specific cost. In this paper, we extend to include special scenarios. First, bug may not have textual report (e.g., crash report) or textual report may lack a topic word (e.g., 1957 of 48,424 in Mozilla reports) Mozilla reports. Second, in some scenarios, developer profiles may change over time. For these scenarios, we extend CosTriage to support non-textual description and dynamic profiles, which we denote as CosTriage+. Our experimental evaluation shows that our solution reduces the cost efficiently by 30 % without seriously compromising accuracy in comparison with the baseline only considering accuracy.

show abstract

Surfacing code in the dark: an instant clone search approach

Cited by 9 publications

References 37 publications

Threshold-free code clone detection for a large-scale heterogeneous Java repository

Threshold-free code clone detection for a large-scale heterogeneous Java repository

Toxic Code Snippets on Stack Overflow

Cost-aware triage ranking algorithms for bug reporting systems

Contact Info

Product

Resources

About