The DOI matches its Crossref record.
Using evolutionary annotations from change logs to enhance program comprehension
Abstract
Evolutionary annotations are descriptions of how source code evolves over time. Typical source comments, given their static nature, are usually inadequate for describing how a program has evolved over time; instead, source code comments are typically a description of what a program currently does. We propose the use of evolutionary annotations as a way of describing the rationale behind changes applied to a given program (for example ”These lines were added to...”). Evolutionary annotations can assist a software developer in the understanding of how a given portion of source code works by showing him how the source has evolved into its current form. In this paper we describe a method to automatically create evolutionary annotations from change logs, defect tracking systems and mailing lists. We describe the design of a prototype for Eclipse that can filter and present these annotations alongside their corresponding source code and in workbench views. We use Apache as a test case to demonstrate the feasibility of this approach.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Logs are a crucial source of information in software systems, capturing system runtime behavior and aiding in software engineering tasks like program comprehension [1], [2], anomaly detection [3]- [6], and failure diagnosis [7], [8].”
From Practitioners’ expectations on log anomaly detection (2025) · cites this paper as [1]
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 6
- 10.1145/1031607.1031622 · DOI
- 10.1002/smr.v16:6 · DOI
- German D. M., Journal of Empirical Software Engineering, 2005. Accepted for publication Sept 25, 2005, to appear in the Special Issue of Best Papers of ICSM 2004.
- 10.1145/1052898.1052912 · DOI
- 10.1023/A:1009778023863 · DOI
- 10.1145/1083142.1083152 · DOI
Cited by
Sorted by citations
Practitioners’ expectations on log anomaly detection
Ma, X., Li, Y., Keung, J., Yu, X., Zou, H., Yang, Z., Sarro, F., & Barr, E. T. (2025) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2025.3586700 · Cited by 21
“L OGS are a crucial source of information in software systems, capturing system runtime behavior and aiding in software engineering tasks like program comprehension [1], [2], anomaly detection [3], [4], [5], [6], and failure diagnosis [7], [8].”
Metadata
- Authors
- German, D. M., Rigby, P. C., & Storey, M.-A.
- Affiliations
- University of Victoria
- Venue
- Proceedings of the 2006 international workshop on Mining software repositories
- Publisher
- ACM
- Published
- 2006-05-22
- Pages
- 159-162
- Cited by
- 2
- References
- 0
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
