A systematic mapping study of clone visualization

Hammad, Muhammad; Basit, Hamid Abdul; Jarzabek, Stan; Koschke, Rainer

doi:10.1016/j.cosrev.2020.100266

Cited by 19 publications

(11 citation statements)

References 127 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Clone-Seeker(Manual) Clone-Seeker(Automatic) Id MRR P@1 P@5 P@10 MRR P@1 P@5 P@10 MRR P@1 P@5 P@10 p00001 0.5 0 0.8 0.9 0.33 0 0.4 0.3 0.5 0 0.8 0.9 p00003 1 1 1 0.9 [25], [82]). We plan to investigate whether our approach can be used for different clone types and granularities in the future.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Clone-Seeker: Effective Code Clone Search Using Annotations

et al. 2022

Self Cite

View full text Add to dashboard Cite

Source code search plays an important role in software development, e.g. for exploratory development or opportunistic reuse of existing code from a code base. Often, exploration of different implementations with the same functionality is needed for tasks like automated software transplantation, software diversification, and software repair. Code clones, which are syntactically or semantically similar code fragments, are perfect candidates for such tasks. Searching for code clones involves a given search query to retrieve the relevant code fragments. We propose a novel approach called Clone-Seeker that focuses on utilizing clone class features in retrieving code clones. For this purpose, we generate metadata for each code clone in the form of a natural language document. The metadata includes a pre-processed list of identifiers from the code clones augmented with a list of keywords indicating the semantics of the code clone. This keyword list can be extracted from a manually annotated general description of the clone class, or automatically generated from the source code of the entire clone class. This approach helps developers to perform code clone search based on a search query written either as source code terms, or as natural language. With various experiments, we show that (1) Clone-Seeker is effective in finding clones from BigCloneBench dataset by applying code queries and natural language queries; 2) Clone-Seeker has a higher recall when searching for semantic code clones (i.e., Type-4) in BigCloneBench than the state-of-the-art; 3) Clone-Seeker is a generalized technique as it is effective in finding clones in Project CodeNet dataset by applying code queries and natural language queries. 4) Clone-Seeker with manual annotation outperforms other variants in finding clones on the basis of natural language queries.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Code clones are usually categorized as Type-1, Type-2, Type-3 and Type-4 (Roy et al, 2009;Hammad et al, 2020b) clones, based on their level of similarity with each other. Type-1 clones are same code fragments, except for dissimilarities in comments, layout, and whitespace.…”

Section: Introductionmentioning

confidence: 99%

Clone-Seeker: Effective Code Clone Search Using Annotations

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…, on a larger dataset or larger pre-trained GPT-2 models). We also plan to investigate how to tackle different types and granularity levels of code clones such as simple clones, structural clones, file clones, and clones of other artifact types such as models ( Babur, Cleophas & van den Brand, 2019 ; Hammad et al, 2020b ; Hammad et al, 2021 ). Moreover, we plan to perform a comparative study by evaluating different IR techniques such as BERT; pretrained word embedding techniques such as word2vec ( Mikolov et al, 2013 ) and GLOVE ( Pennington, Socher & Manning, 2014 ); and code query formulation techniques ( Lu et al, 2015 , Nie et al, 2016 ).…”

Section: Discussionmentioning

confidence: 99%

“… Abid (2019) have previously used clone methods for code recommendation, however, the approach used a different similarity measure based on API calls for recommending relevant clone methods. Clones are generally considered to be harmful for a software system, and mainly researchers work on techniques for avoiding and eliminating clones ( Yoshida et al, 2019 ; Wang & Godfrey, 2014 ; Basit et al, 2015 ; Basit, Hammad & Koschke, 2015 ; Hammad et al, 2020b ). Clone refactoring recommendation systems have been developed for this purpose.…”

Section: Related Workmentioning

confidence: 99%

Clone-advisor: recommending code tokens and clone methods with deep learning and information retrieval

Hammad

Babur

Basit

et al. 2021

PeerJ Computer Science

Self Cite

View full text Add to dashboard Cite

Software developers frequently reuse source code from repositories as it saves development time and effort. Code clones (similar code fragments) accumulated in these repositories represent often repeated functionalities and are candidates for reuse in an exploratory or rapid development. To facilitate code clone reuse, we previously presented DeepClone, a novel deep learning approach for modeling code clones along with non-cloned code to predict the next set of tokens (possibly a complete clone method body) based on the code written so far. The probabilistic nature of language modeling, however, can lead to code output with minor syntax or logic errors. To resolve this, we propose a novel approach called Clone-Advisor. We apply an information retrieval technique on top of DeepClone output to recommend real clone methods closely matching the predicted clone method, thus improving the original output by DeepClone. In this paper we have discussed and refined our previous work on DeepClone in much more detail. Moreover, we have quantitatively evaluated the performance and effectiveness of Clone-Advisor in clone method recommendation.

show abstract

“…is information helps the practitioners, researchers, and organizations in a certain way [12][13][14][15]. e set of DQs is as follows: DQ1: who are the most active researchers in the field of analyzing election prediction on Twitter?…”

Section: Aim and Research Questionsmentioning

confidence: 99%

Election Prediction on Twitter: A Systematic Mapping Study

et al. 2021

View full text Add to dashboard Cite

Context. Social media platforms such as Facebook and Twitter carry a big load of people’s opinions about politics and leaders, which makes them a good source of information for researchers to exploit different tasks that include election predictions. Objective. Identify, categorize, and present a comprehensive overview of the approaches, techniques, and tools used in election predictions on Twitter. Method. Conducted a systematic mapping study (SMS) on election predictions on Twitter and provided empirical evidence for the work published between January 2010 and January 2021. Results. This research identified 787 studies related to election predictions on Twitter. 98 primary studies were selected after defining and implementing several inclusion/exclusion criteria. The results show that most of the studies implemented sentiment analysis (SA) followed by volume-based and social network analysis (SNA) approaches. The majority of the studies employed supervised learning techniques, subsequently, lexicon-based approach SA, volume-based, and unsupervised learning. Besides this, 18 types of dictionaries were identified. Elections of 28 countries were analyzed, mainly USA (28%) and Indian (25%) elections. Furthermore, the results revealed that 50% of the primary studies used English tweets. The demographic data showed that academic organizations and conference venues are the most active. Conclusion. The evolution of the work published in the past 11 years shows that most of the studies employed SA. The implementation of SNA techniques is lower as compared to SA. Appropriate political labelled datasets are not available, especially in languages other than English. Deep learning needs to be employed in this domain to get better predictions.

show abstract

A systematic mapping study of clone visualization

Cited by 19 publications

References 127 publications

Clone-Seeker: Effective Code Clone Search Using Annotations

Clone-Seeker: Effective Code Clone Search Using Annotations

Clone-advisor: recommending code tokens and clone methods with deep learning and information retrieval

Election Prediction on Twitter: A Systematic Mapping Study

Contact Info

Product

Resources

About