Source Code Metrics for Software Defects Prediction
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F23%3A00130144" target="_blank" >RIV/00216224:14330/23:00130144 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1145/3555776.3577809" target="_blank" >http://dx.doi.org/10.1145/3555776.3577809</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3555776.3577809" target="_blank" >10.1145/3555776.3577809</a>
Alternative languages
Result language
angličtina
Original language name
Source Code Metrics for Software Defects Prediction
Original language description
In current research, there are contrasting results about the applicability of software source code metrics as features for defect prediction models. The goal of the paper is to evaluate the adoption of software metrics in models for software defect prediction, identifying the impact of individual source code metrics. With an empirical study on 275 release versions of 39 Java projects mined from GitHub, we compute 12 software metrics and collect software defect information. We train and compare three defect classification models. The results across all projects indicate that Decision Tree (DT) and Random Forest (RF) classifiers show the best results. Among the highest-performing individual metrics are NOC, NPA, DIT, and LCOM5. While other metrics, such as CBO, do not bring significant improvements to the models.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
<a href="/en/project/EF16_019%2F0000822" target="_blank" >EF16_019/0000822: CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
The 38th ACM/SIGAPP Symposium on Applied Computing (SAC '23)
ISBN
9781450395175
ISSN
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e-ISSN
—
Number of pages
4
Pages from-to
1469-1472
Publisher name
Association for Computing Machinery (ACM)
Place of publication
Not specified
Event location
Tallinn, Estonia
Event date
Mar 27, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001124308100207