Proceedings Article10.1145/3377811.3380405
Software documentation: the practitioners' perspective
Emad Aghajani,Csaba Nagy,Mario Linares-Vasquez,Laura Moreno,Gabriele Bavota,Michele Lanza,David C. Shepherd +6 more
- 27 Jun 2020
- pp 590-601
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TL;DR: Two surveys are presented to investigate the documentation issues practitioners perceive as more relevant together with solutions they apply when these issues arise and the types of documentation considered as important in different tasks, which can help researchers in designing the next generation of documentation recommender systems.
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Abstract: In theory, (good) documentation is an invaluable asset to any software project, as it helps stakeholders to use, understand, maintain, and evolve a system. In practice, however, documentation is generally affected by numerous shortcomings and issues, such as insufficient and inadequate content and obsolete, ambiguous information. To counter this, researchers are investigating the development of advanced recommender systems that automatically suggest high-quality documentation, useful for a given task. A crucial first step is to understand what quality means for practitioners and what information is actually needed for specific tasks. We present two surveys performed with 146 practitioners to investigate (i) the documentation issues they perceive as more relevant together with solutions they apply when these issues arise; and (ii) the types of documentation considered as important in different tasks. Our findings can help researchers in designing the next generation of documentation recommender systems.
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Citations
Knowledge-based approaches in software documentation: A systematic literature review
TL;DR: There is a need to use knowledge-based approaches to improve the quality attributes of software documents that receive less attention, especially credibility, conciseness, and unambiguity.
An Empirical Study on Deployment Faults of Deep Learning Based Mobile Applications
Zhenpeng Chen,Huihan Yao,Yiling Lou,Yanbin Cao,Yuanqiang Liu,Haoyu Wang,Xuanzhe Liu +6 more
- 22 May 2021
TL;DR: Wang et al. as mentioned in this paper presented the first comprehensive study to date on the deployment faults of mobile DL apps, identifying 304 real deployment faults from Stack Overflow and GitHub, two commonly used data sources for studying software faults, and constructed a fine-granularity taxonomy consisting of 23 categories regarding to fault symptoms and distill common fix strategies for different fault symptoms.
Automatic Code Documentation Generation Using GPT-3
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- 06 Sep 2022
TL;DR: Codec is a GPT-3 based model pre-trained on both natural and programming languages that outperforms existing techniques even with basic settings like one-shot learning and achieves an overall BLEU score of 20.6 for six different programming languages.
How to fight production incidents?: an empirical study on a large-scale cloud service
Supriyo Ghosh,Manish Shetty,Chetan Bansal,Suman Nath +3 more
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TL;DR: Wang et al. as mentioned in this paper study hundreds of recent high severity incidents and their postmortems in Microsoft-Teams, a large-scale distributed cloud based service used by hundreds of millions of users.
42
Practitioners' Expectations on Automated Code Comment Generation
Xing Hu,Xin Xia,David Lo,Zhiyuan Wan,Qiuyuan Chen,Thomas Zimmermann +5 more
- 01 May 2022
TL;DR: What practitioners need and the current state-of-the-art research in comment generation are compared by performing a literature review of papers on code comment generation techniques pub-lished in the premier publication venues from 2010 to 2020 and highlighting the directions where researchers need to put effort.
41
References
Comparison and evaluation of code clone detection techniques and tools: A qualitative approach
TL;DR: A qualitative comparison and evaluation of the current state-of-the-art in clone detection techniques and tools is provided, and a taxonomy of editing scenarios that produce different clone types and a qualitative evaluation of current clone detectors are evaluated.
1.1K
Recovering traceability links between code and documentation
TL;DR: A probabilistic and a vector space information retrieval model is applied in two case studies to trace C++ source code onto manual pages and Java code to functional requirements to recover traceability links between source code and free text documents.
•Book
Guide to the Software Engineering Body of Knowledge - SWEBOK
Alain Abran,Pierre Bourque,Robert Dupuis,James W. Moore +3 more
- 01 Dec 2001
TL;DR: The mapping shows that, though there are no major "school of thought" divergences between the two bodies of knowledge, there are a number of differences in the details of each breakdown in terms of vocabulary, level of detail, decomposition approach and topics encompassed.
1K
Deep code comment generation
TL;DR: DeepCom applies Natural Language Processing (NLP) techniques to learn from a large code corpus and generates comments from learned features for better comments generation of Java methods.
857
DECOR: A Method for the Specification and Detection of Code and Design Smells
TL;DR: DETEX is proposed, a method that embodies and defines all the steps necessary for the specification and detection of code and design smells, and a detection technique that instantiates this method, and an empirical validation in terms of precision and recall of DETEX.