Mining StackOverflow to Filter Out Off-Topic IRC Discussion

Cited by 34 publications

(15 citation statements)

References 7 publications

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“…Moreover, we find that the query redundancy of TUV is relatively high. This suggests that it is not appropriate to extract the software repositories via utilizing their social features of “upvotes” or “pageviews” in recent work . In addition, MR has medium query redundancy on 20 000 samples, which may be the changing trend of the curve is not apparent.…”

Section: Methodsmentioning

confidence: 99%

“…For example, Ponzanelli et al created the models for retrieving pertinent discussions from StackOverflow to turn the integrated development environment into a self‐confident programming prompter. Chowdhury and Hindle trained a classifier to filter out off‐topic messages of StackOverflow for Internet Relay Chat discussion. Chen et al proposed a programming language independent method for code pattern recognition based on code patterns extracted from Stack Overflow.…”

Section: Related Workmentioning

confidence: 99%

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Unsupervised software repositories mining and its application to code search

Peng

Yihan

et al. 2019

Softw Pract Exp

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Software repositories are crucial resources for many software tasks, including code retrieval and annotation. Programming forums provide questions and answers (Q&A) from software developers, containing abundant code-description posts for exchanging knowledge about programming issues. However, most posts provide personal opinions of users that are often not adequately confirmed or outdated. Mining software repositories in such open and unrestricted forums is challenging. Since the posts can be arbitrary and noisy, it is difficult to get unified labels for supervised noise elimination. Different from existing mining approaches, this paper proposes Code-Description Mining Framework (CodeMF), an unsupervised framework to eliminate noisy posts and extract high quality software repositories from programming forums.CodeMF treats all social features of the posts as discrete-time signals for kernel principal component analysis and further performs wavelet transform feature fusion to find the delicate changes (noises in temporal signals). We conduct comprehensive experiments on StackOverflow. Experimental results demonstrate that CodeMF can effectively reduce running time and improve precision via mining high-quality software repositories for various programming languages, especially for the large-scale codebases. To further illustrate the effect of CodeMF applied in software tasks, we introduce it to improve the performance of query-expansion code search. Meanwhile, for SQL and C# programs, compared to the state-of-the-art query-expansion method QECK, the improvement of QECK CodeMF is 2% and 6% on Recall@10, and 4% and 14% on mean reciprocal rank, respectively.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Unsupervised software repositories mining and its application to code search

Peng

Yihan

et al. 2019

Softw Pract Exp

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“…Honsel et al [55] evaluated common myths about SO posts to discover whether the perception of software developers is accurate. Chowdhury and Hindle [56] proposed an approach to classify off-topic SO posts in programming-related internet relay chat channels. Li et al [57] empirically investigated to identify the diverse needs and problems software developers face.…”

Section: Martinez and Lecomtementioning

confidence: 99%

Toward Empirically Investigating Non-Functional Requirements of iOS Developers on Stack Overflow

Ahmad

Feng

et al. 2019

IEEE Access

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Context: Mobile application developers are getting more concerned due to the importance of quality requirements or non-functional requirements (NFR) in software quality. Developers around the globe are actively asking a question(s) and sharing solutions to the problems related to software development on Stack Overflow (SO). The knowledge shared by developers on SO contains useful information related to software development such as feature requests (functional/non-functional), code snippets, reporting bugs or sentiments. Extracting the NFRs shared by iOS developers on programming Q&A website SO has become a challenge and a less researched area. Objective: To identify and understand the real problems, needs, trends, and the critical NFRs or quality requirements discussed on Stack Overflow related to iOS mobile application development. Method: We extracted and used only the iOS posts data of SO. We applied the well-known statistical topical model Latent Dirichlet Allocation (LDA) to identify the main topics in iOS posts on SO. Then, we labeled the extracted topics with quality requirements or NFRs by using the wordlists to assess the trend, evolution, hot and unresolved NFRs in all iOS discussions. Results: Our findings revealed that the highly frequent topics the iOS developers discussed are related to usability, reliability, and functionality followed by efficiency. Interestingly, the most problematic areas unresolved are also usability, reliability, and functionality though followed by portability. Besides, the evolution trend of each of the six different quality requirements or NFRs over time is depicted through comprehensive visualization. Conclusion: Our first empirical investigation on approximately 1.5 million iOS posts and comments of SO gives insight on comprehending the NFRs in iOS application development through the lens of real-world practitioners. INDEX TERMS Non-functional requirements (NFRs), quality requirements, iOS, Latent Dirichlet allocation (LDA), Stack Overflow.

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“…Classifying chat messages into two classes, as in our experiment, is known as binary classification. We compared the performance of two learning algorithms, Naive Bayes Multinomial and Support Vector Machine (SVM) due to their popularity and good performance for text classification [11], [18], [39]. a) Preprocessing: Before training the classifiers, we preprocessed the message text by converting it into tokens and lowercase.…”

Section: A Binary Classificationmentioning

confidence: 99%

“…In their work, they mined IRC meeting logs to investigate the meeting content, meeting participants, their contribution and communication styles. Chowdhury and Hindle [11] implemented machine learning approaches to filter out off-topic discussions in programming IRC channels by exploiting StackOverflow programming discussions and YouTube video comments. Yu et al [45] investigated the use of synchronous (IRC) and asynchronous (mailing list) communication mechanisms in global software development projects.…”

Section: B Mining Developers' Communication Artifactsmentioning

confidence: 99%

Rationale in Development Chat Messages: An Exploratory Study

Alkadhi

Lata

Guzmany

et al. 2017

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

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Chat messages of development teams play an increasingly significant role in software development, having replaced emails in some cases. Chat messages contain information about discussed issues, considered alternatives and argumentation leading to the decisions made during software development. These elements, defined as rationale, are invaluable during software evolution for documenting and reusing development knowledge. Rationale is also essential for coping with changes and for effective maintenance of the software system. However, exploiting the rationale hidden in the chat messages is challenging due to the high volume of unstructured messages covering a wide range of topics. This work presents the results of an exploratory study examining the frequency of rationale in chat messages, the completeness of the available rationale and the potential of automatic techniques for rationale extraction. For this purpose, we apply content analysis and machine learning techniques on more than 8,700 chat messages from three software development projects. Our results show that chat messages are a rich source of rationale and that machine learning is a promising technique for detecting rationale and identifying different rationale elements.

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Mining StackOverflow to Filter Out Off-Topic IRC Discussion

Cited by 34 publications

References 7 publications

Unsupervised software repositories mining and its application to code search

Unsupervised software repositories mining and its application to code search

Toward Empirically Investigating Non-Functional Requirements of iOS Developers on Stack Overflow

Rationale in Development Chat Messages: An Exploratory Study

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