Improving the Search for Architecture Knowledge in Online Developer Communities

Soliman, Mohamed; Salama, Amr Rekaby; Galster, Matthias; Zimmermann, Olaf; Riebisch, Matthias

doi:10.1109/icsa.2018.00028

Cited by 23 publications

(29 citation statements)

References 33 publications

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“…Mining information related to software architecture from open source software repositories is not new. Other researchers have shown that such information can be derived from commit logs (either manually [35] or by applying machine learning [5]), issue trackers [24], as well as developer discussions in community groups such as StackOverflow [34] or chat groups [3]. In this paper, we have confirmed this by surveying developers to verify the usefulness of the architectural guidelines.…”

Section: Related Worksupporting

confidence: 66%

How do you architect your robots?

Malavolta

Lewis

Schmerl

et al. 2020

Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Software Engineering in Practice

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Section: Related Worksupporting

confidence: 66%

How do you architect your robots?

Malavolta

Lewis

Schmerl

et al. 2020

Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Software Engineering in Practice

View full text Add to dashboard Cite

show abstract

“…One of the significant challenges present in many commit messages is developers express more than one concept (contextual occurrence of words) for a single intention. An N-gram model might capture continuous sequences of nwords involved in such concepts within a sentence [35], [36]. However, in multiple iterations of our inspection, we find that concept words are scattered among multiple sentences in many commit descriptions.…”

Section: B Change Classification From Textmentioning

confidence: 89%

“…The configuration of our DNL model has 64 layers, 64 units, epoch size 10, relu activation function, and cross-entropy as loss function [36]. Furthermore, we adopted the best algorithms suggested by Hindle et al [14] to classify large change commits into five categories, and Soliman et al [35] to classify architectural discussions. We also explore Naive Bayes (N B), Decision Trees (DT ), and Random Forest (RF ) [23], [14], [35] for our dataset with the WEKA [13] tool utilizing word-to-vector features [27].…”

Section: Performance Evaluationmentioning

confidence: 99%

ArchiNet: A Concept-token based Approach for Determining Architectural Change Categories

Kumar¹

2021

Proceedings of the 33rd International Conference on Software Engineering and Knowledge Engineering

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Causes of software architectural change are classified as perfective, preventive, corrective, and adaptive. Change classification is used to promote common approaches for addressing similar changes, produce appropriate design documentation for a release, construct a developer's profile, form a balanced team, support code review, etc. However, automated architectural change classification techniques are in their infancy, perhaps due to the lack of a benchmark dataset and the need for extensive human involvement. To address these shortcomings, we present a benchmark dataset and a text classifier for determining the architectural change rationale from commit descriptions. First, we explored source code properties for change classification independent of project activity descriptions and found poor outcomes. Next, through extensive analysis, we identified the challenges of classifying architectural change from text and proposed a new classifier that uses concept tokens derived from the concept analysis of change samples. We also studied the sensitivity of change classification of various types of tokens present in commit messages. The experimental outcomes employing 10fold and cross-project validation techniques with five popular open-source systems show that the F1 score of our proposed classifier is around 70%. The precision and recall are mostly consistent among all categories of change and more promising than competing methods for text classification.

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“…Bagheri and Ensan [14] propose a semantic tagging approach based on Wikipedia data to improve Stack Overflow tags, eventually improving the search process. Soliman et al's [15] approach on the other hand is more domain-specific, focusing on how architects search for architecturally relevant information on Stack Overflow. CROKAGE by Silva et al [16] takes the description of a programming task and provides a comprehensive solution for this task by searching multiple threads.…”

Section: Related Workmentioning

confidence: 99%

Essential Sentences for Navigating Stack Overflow Answers

Nadi

2020

2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)

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Stack Overflow (SO) has become an essential resource for software development. Despite its success and prevalence, navigating SO remains a challenge. Ideally, SO users could benefit from highlighted navigational cues that help them decide if an answer is relevant to their task and context. Such navigational cues could be in the form of essential sentences that help the searcher decide whether they want to read the answer or skip over it. In this paper, we compare four potential approaches for identifying essential sentences. We adopt two existing approaches and develop two new approaches based on the idea that contextual information in a sentence (e.g., "if using windows") could help identify essential sentences. We compare the four techniques using a survey of 43 participants. Our participants indicate that it is not always easy to figure out what the best solution for their specific problem is, given the options, and that they would indeed like to easily spot contextual information that may narrow down the search. Our quantitative comparison of the techniques shows that there is no single technique sufficient for identifying essential sentences that can serve as navigational cues, while our qualitative analysis shows that participants valued explanations and specific conditions, and did not value filler sentences or speculations. Our work sheds light on the importance of navigational cues, and our findings can be used to guide future research to find the best combination of techniques to identify such cues.

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Improving the Search for Architecture Knowledge in Online Developer Communities

Cited by 23 publications

References 33 publications

How do you architect your robots?

How do you architect your robots?

ArchiNet: A Concept-token based Approach for Determining Architectural Change Categories

Essential Sentences for Navigating Stack Overflow Answers

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