loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Adam Roman ; Rafał Brożek and Jarosław Hryszko

Affiliation: Jagiellonian University, Faculty of Mathematics and Computer Science, Poland

Keyword(s): Software Defect Prediction, Data Flow Metrics, Dep-Degree, Data Flow Complexity, Within-Project Defect Prediction.

Abstract: Data flow coverage criteria are widely used in software testing, but there is almost no research on low-level data flow metrics as software defect predictors. Aims: We examine two such metrics in this context: dep- degree (DD) proposed by Beyer and Fararooy and a new data flow metric called dep-degree density (DDD). Method: We investigate the importance of DD and DDD in SDP models. We perform a correlation analysis to check if DD and DDD measure different aspects of the code than the well-known size, complexity, and documentation metrics. Finally, we perform experiments with five different classifiers on nine projects from the Unified Bug Dataset to compare the performance of the SDP models trained with and without data flow metrics. Results: 1) DD is noticeably correlated with many other code metrics, but DDD is not correlated or is very weakly correlated with other metrics considered in this study; 2) both DD and DDD are highly ranked in the feature importance analysis; 3) SDP mode ls that use DD and DDD perform better than models that do not use data flow metrics. Conclusions: Data-flow metrics: DD and DDD can be valuable predictors in SDP models. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.144.19.182

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Roman, A., Brożek, R. and Hryszko, J. (2023). Predictive Power of Two Data Flow Metrics in Software Defect Prediction. In Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE; ISBN 978-989-758-647-7; ISSN 2184-4895, SciTePress, pages 114-125. DOI: 10.5220/0011842200003464

@conference{enase23,
author={Adam Roman and Rafał Brożek and Jarosław Hryszko},
title={Predictive Power of Two Data Flow Metrics in Software Defect Prediction},
booktitle={Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE},
year={2023},
pages={114-125},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011842200003464},
isbn={978-989-758-647-7},
issn={2184-4895},
}

TY - CONF

JO - Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE
TI - Predictive Power of Two Data Flow Metrics in Software Defect Prediction
SN - 978-989-758-647-7
IS - 2184-4895
AU - Roman, A.
AU - Brożek, R.
AU - Hryszko, J.
PY - 2023
SP - 114
EP - 125
DO - 10.5220/0011842200003464
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: [email protected]

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic