Source code retrieval on StackOverflow using LDA

Cited by 16 publications

(11 citation statements)

References 10 publications

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“…In isolation, SO has been the subject of various research studies. One example is the use of topic modeling on SO questions to categorize discussions [1]- [3], another is the use SO statistics to analyze use behavior and activity [4], [5]. Recent work has paid special attention to code snippets.…”

Section: Introductionmentioning

confidence: 99%

Stack Overflow in Github: Any Snippets There?

Yang

Martins

Saini

et al. 2017

2017 IEEE/ACM 14th International Conference on Mining Software Repositories (MSR)

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When programmers look for how to achieve certain programming tasks, Stack Overflow is a popular destination in search engine results. Over the years, Stack Overflow has accumulated an impressive knowledge base of snippets of code that are amply documented. We are interested in studying how programmers use these snippets of code in their projects. Can we find Stack Overflow snippets in real projects? When snippets are used, is this copy literal or does it suffer adaptations? And are these adaptations specializations required by the idiosyncrasies of the target artifact, or are they motivated by specific requirements of the programmer? The large-scale study presented on this paper analyzes 909k non-fork Python projects hosted on Github, which contain 290M function definitions, and 1.9M Python snippets captured in Stack Overflow. Results are presented as quantitative analysis of block-level code cloning intra and inter Stack Overflow and GitHub, and as an analysis of programming behaviors through the qualitative analysis of our findings.

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Section: Introductionmentioning

confidence: 99%

Stack Overflow in Github: Any Snippets There?

Yang

Martins

Saini

et al. 2017

2017 IEEE/ACM 14th International Conference on Mining Software Repositories (MSR)

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“…Besides, it is capable of conducting effective searching on SO even with less articulated information provided in the questions posted. Similarly, Arwan et al (2015) proposed a method for discovering code on SO by making use of concept location in the data preprocessing step. They used LDA for creating inference concept location from the source code.…”

Section: Mining So For Software Developmentmentioning

confidence: 99%

A survey on mining stack overflow: question and answering (Q&A) community

Ahmad

Feng

Shi

et al. 2018

DTA

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Purpose Software developers extensively use stack overflow (SO) for knowledge sharing on software development. Thus, software engineering researchers have started mining the structured/unstructured data present in certain software repositories including the Q&A software developer community SO, with the aim to improve software development. The purpose of this paper is show that how academics/practitioners can get benefit from the valuable user-generated content shared on various online social networks, specifically from Q&A community SO for software development. Design/methodology/approach A comprehensive literature review was conducted and 166 research papers on SO were categorized about software development from the inception of SO till June 2016. Findings Most of the studies revolve around a limited number of software development tasks; approximately 70 percent of the papers used millions of posts data, applied basic machine learning methods, and conducted investigations semi-automatically and quantitative studies. Thus, future research should focus on the overcoming existing identified challenges and gaps. Practical implications The work on SO is classified into two main categories; “SO design and usage” and “SO content applications.” These categories not only give insights to Q&A forum providers about the shortcomings in design and usage of such forums but also provide ways to overcome them in future. It also enables software developers to exploit such forums for the identified under-utilized tasks of software development. Originality/value The study is the first of its kind to explore the work on SO about software development and makes an original contribution by presenting a comprehensive review, design/usage shortcomings of Q&A sites, and future research challenges.

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“…The PHP was the most popular server-side programming language over the world [8]. The Faculties on University of Brawijaya used web-based open source technology to create web application.…”

Section: Introductionmentioning

confidence: 99%

Maintenance Web Based Applications Using Feature Location

Arwan

Rusdianto

2020

JITeCS

Self Cite

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Maintenance web applications are a complex set of efforts. The FilkomApps are the web application used by the Faculty of Computer Science of Universitas Brawijaya to arrange the academic, theses of students, assignment of faculty, inventory, presence, honorarium. It has about 6K number of files(HTML, PHP, JS, CSS). The feature location was able to help the maintenance of the web applications by locating specific features on the files. The process comprises of preprocessing (tokenizing, web language syntax removal, splitting, stopword and stemming), indexing (VSM Lucene), and evaluations (precision and recall). The experiments were done by querying the keywords originate from previous maintenance modification effort and feature of a system. The results of precision were 86% and recall were 47%. The precision was better 374% than the conventional method (using the IDE search feature)

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Source code retrieval on StackOverflow using LDA

Cited by 16 publications

References 10 publications

Stack Overflow in Github: Any Snippets There?

Stack Overflow in Github: Any Snippets There?

A survey on mining stack overflow: question and answering (Q&A) community

Maintenance Web Based Applications Using Feature Location

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