The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A study of the documentation essential to software maintenance
Abstract
Software engineering has been striving for years to improve the practice of software development and maintenance. Documentation has long been prominent on the list of recommended practices to improve development and help maintenance. Recently however, agile methods started to shake this view, arguing that the goal of the game is to produce software and that documentation is only useful as long as it helps to reach this goal.On the other hand, in the re-engineering field, people wish they could re-document useful legacy software so that they may continue maintain them or migrate them to new platform.In these two case, a crucial question arises: "How much documentation is enough?" In this article, we present the results of a survey of software maintainers to try to establish what documentation artifacts are the most useful to them.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The relevance of software documentation, tools and technologies: a survey
Forward, A., & Lethbridge, T. C. (2002) · Proceedings of the 2002 ACM symposium on Document engineering · doi:10.1145/585058.585065 · Cited by 268
Cited by
Wang, Y., Wang, W., Joty, S., & Hoi, S. C. H. (2021) · arXiv · doi:10.48550/arxiv.2109.00859 · Cited by 1,495
Wang, Y., Wang, W., Joty, S., & Hoi, S. C. H. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.685 · Cited by 1,495
Towards automatically generating summary comments for Java methods
Sridhara, G., Hill, E., Muppaneni, D., Pollock, L., & Vijay-Shanker, K. (2010) · Proceedings of the IEEE/ACM international conference on Automated software engineering · doi:10.1145/1858996.1859006 · Cited by 514
Curating GitHub for engineered software projects
Munaiah, N., Kroh, S., Cabrey, C., & Nagappan, M. (2017) · Empirical Software Engineering · doi:10.1007/s10664-017-9512-6 · Cited by 365
Automated Grading and Feedback Tools for Programming Education: A Systematic Review
Messer, M., Brown, N. C. C., Kölling, M., & Shi, M. (2024) · ACM Transactions on Computing Education · doi:10.1145/3636515 · Cited by 262
Automatically documenting program changes
Buse, R. P. L., & Weimer, W. R. (2010) · Proceedings of the IEEE/ACM international conference on Automated software engineering · doi:10.1145/1858996.1859005 · Cited by 219
Improving automated source code summarization via an eye-tracking study of programmers
Rodeghero, P., McMillan, C., McBurney, P. W., Bosch, N., & D'Mello, S. (2014) · Proceedings of the 36th International Conference on Software Engineering · doi:10.1145/2568225.2568247 · Cited by 198
Cost, benefits and quality of software development documentation: A systematic mapping
Zhi, J., Garousi-Yusifoğlu, V., Sun, B., Garousi, G., Shahnewaz, S., & Ruhe, G. (2015) · Journal of Systems and Software · doi:10.1016/j.jss.2014.09.042 · Cited by 112
A Large-Scale Empirical Study on Code-Comment Inconsistencies
Wen, F., Nagy, C., Bavota, G., & Lanza, M. (2019) · 2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC) · doi:10.1109/icpc.2019.00019 · Cited by 105
Retrieve and refine: exemplar-based neural comment generation
Wei, B., Li, Y., Li, G., Xia, X., & Jin, Z. (2020) · Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering · doi:10.1145/3324884.3416578 · Cited by 69
Automating just-in-time comment updating
Liu, Z., Xia, X., Yan, M., & Li, S. (2020) · Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering · doi:10.1145/3324884.3416581 · Cited by 56
The Value of Software Documentation Quality
Plosch, R., Dautovic, A., & Saft, M. (2014) · 2014 14th International Conference on Quality Software · doi:10.1109/qsic.2014.22 · Cited by 44
A mapping study on documentation in Continuous Software Development
Theunissen, T., van Heesch, U., & Avgeriou, P. (2022) · Information and Software Technology · doi:10.1016/j.infsof.2021.106733 · Cited by 40
Wang, A. Y., Wang, D., Drozdal, J., Liu, X., Park, S., Oney, S., & Brooks, C. (2021) · Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411763.3451617 · Cited by 31
Deep Just-In-Time Inconsistency Detection Between Comments and Source Code
Panthaplackel, S., Li, J. J., Gligoric, M., & Mooney, R. J. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i1.16119 · Cited by 2
Metadata
- Authors
- de Souza, S. C. B., Anquetil, N., & de Oliveira, K. M.
- Venue
- Proceedings of the 23rd annual international conference on Design of communication: documenting & designing for pervasive information
- Publisher
- ACM
- Published
- 2005-09-21
- Pages
- 68-75
- OpenAlex
- W2133890582
- Cited by
- 305
- References
- 1
- FWCI
- 2.26
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
