How are issue reports discussed in Gitter chat rooms?

Sahar, Hareem; Hindle, Abram; Bezemer, Cor‐Paul

doi:10.1016/j.jss.2020.110852

Cited by 16 publications

(5 citation statements)

References 10 publications

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“…Sahar et al work focused on the analysis of how developers discuss issue reports in Gitter chat rooms related to opensource systems [3]. The authors proposed an approach involving clustering algorithms to identify issue report discussions automatically.…”

Section: A Instant Messaging and Communication Tools For Software Dev...mentioning

confidence: 99%

Comparative Analysis of Word Embedding and Machine Learning Techniques for Classification of Software Developer Communications on Gitter

Akshar,

Kumar,

Yogita

et al. 2023

Annals of Computer Science and Information Systems

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In recent times, software developers widely use instant messaging and collaboration platforms, as these platforms aid them in exploring new technologies, raising different development-related issues, and seeking solutions from their peers virtually. Gitter is one such platform that has a heavy userbase. It generates a tremendous volume of data, analysis of which is helpful to gain insights about trends in open-source software development and the developers' inclination toward various technologies. Analyzing these trends helps these platforms better cater to the needs of the developers, in turn increasing the usage of these platforms and promoting collaborations between more developers. The classification techniques can be deployed for this purpose. The selection of an apt word embedding for a given dataset of text messages plays a vital role in determining the performance of classification techniques. In the present work, the comparative analysis of nine-word embeddings in combination with seventeen classification techniques with onevsone and onevsrest has been performed on the GitterCom dataset for categorizing text messages into one of the pre-determined classes based on their purpose. Further, two feature selection methods have been applied. The SMOTE technique has been used for handling data imbalance. It resulted in a total of 1836 classification pipelines for analysis. The objective is to analyze their performances to recommend efficient pipelines for the classification task at hand. The experimental results show that word2vect, GLOVE with 300 vector size, and GLOVE with 100 vector size are three topperforming word embeddings having performance values taken across different classification techniques. The models trained using ANOVA features performed similarly to those models trained using all features. Finally, using the SMOTE technique helps models to get a better prediction ability.

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Section: A Instant Messaging and Communication Tools For Software Dev...mentioning

confidence: 99%

Comparative Analysis of Word Embedding and Machine Learning Techniques for Classification of Software Developer Communications on Gitter

Akshar,

Kumar,

Yogita

et al. 2023

Annals of Computer Science and Information Systems

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“…The authors found that the largest proportion of Slack conversations are "design"-related conversations (which involve discussions of API usage and recommendations), followed by "explanatory"-type of conversations (e.g., developers explaining to each other technologies and capabilities of different programming languages). Sahar et al [97] studied how developers discuss issue reports and found that issue reports discussed in Gitter take more time to get resolved compared to issue reports that are not discussed. Using thematic analysis, Mezouar et al [98] identified the reasons behind the use of Slack and Gitter, the perceived impact on the associated projects and the quality characteristics of these instant messaging tools.…”

Section: Existing Research On Chat Platformsmentioning

confidence: 99%

Identifying Opinion Based Questions in Developer Chat Communication

Subash¹

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Today, software developers work on complex and fast-moving projects that often require instant assistance from other domain and subject matter experts. Chat servers such as Discord facilitate live communication and collaboration among developers all over the world. With numerous topics discussed in parallel, mining and analyzing the chat data of these platforms would offer researchers and tool makers opportunities to develop software tools and services such as automated virtual assistants, chatbots, chat summarization techniques, Q&A thesaurus, and more.

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“…With the rise of modern chat platforms such as Gitter, Slack, and Discord, recent research efforts gave been focused on exploring the developer communities using these tools [10,104,153,6,5,89,112,74,33,31,48,138,32,128,118,155].…”

Section: Instant Messaging Platforms Used By Software Developersmentioning

confidence: 99%

“…This resulted in a set of 2,939,335 messages across all 139 channels. The data gathering procedure is similar to the one followed by [138]. We then excluded the channels in which the conversations were not in English.…”

Section: The Gittercom Datasetmentioning

confidence: 99%

Aiding Software Developers' Information Seeking in Modern Communication Mediums

DigiNole

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Software developers generally need a variety of information to efficiently perform their daily tasks, such as information regarding programming languages and constructs, algorithms, platforms, APIs, programming errors, etc. Traditionally, developers obtain information from mediums such as source code, version control repositories, co-workers, internal communication emails, and online documentation. However, nowadays, besides the traditional information mediums available to developers, software developers have access to multiple modern communication mediums that capture knowledge and information in a variety of formats, such as online forums, videos, and online chat platforms, among others.

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How are issue reports discussed in Gitter chat rooms?

Cited by 16 publications

References 10 publications

Comparative Analysis of Word Embedding and Machine Learning Techniques for Classification of Software Developer Communications on Gitter

Comparative Analysis of Word Embedding and Machine Learning Techniques for Classification of Software Developer Communications on Gitter

Identifying Opinion Based Questions in Developer Chat Communication

Aiding Software Developers' Information Seeking in Modern Communication Mediums

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