Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 580

The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.

Predicting defects using network analysis on dependency graphs

Zimmermann, Thomas, Nagappan, Nachiappan·2008·Proceedings of the 13th international conference on Software engineering - ICSE '08

Abstract

In software development, resources for quality assurance are limited by time and by cost. In order to allocate resources effectively, managers need to rely on their experience backed by code complexity metrics. But often dependencies exist between various pieces of code over which managers may have little knowledge. These dependencies can be construed as a low level graph of the entire system. In this paper, we propose to use network analysis on these dependency graphs. This allows managers to identify central program units that are more likely to face defects. In our evaluation on Windows Server 2003, we found that the recall for models built from network measures is by 10% points higher than for models built from complexity metrics. In addition, network measures could identify 60% of the binaries that the Windows developers considered as critical-twice as many as identified by complexity metrics.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1145/1368088.1368161

Cites

38 of 38 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Show all 38 references

Cited by

See all 580

Metadata

Authors
Zimmermann, T., & Nagappan, N.
Venue
Proceedings of the 13th international conference on Software engineering - ICSE '08
Publisher
ACM Press
Published
2008
Pages
531
ISSN
0270-5257 (ISSN-L)
OpenAlex
W2135198476
Cited by
580
References
38
FWCI
28.59
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

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Predicting defects using network analysis on dependency graphs. https://app.cochat.ai/papers/10.1145/1368088.1368161
The paper
Zimmermann, T., & Nagappan, N. (2008). Predicting defects using network analysis on dependency graphs. Proceedings of the 13th international conference on Software engineering - ICSE '08. https://doi.org/10.1145/1368088.1368161

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.