skip to main content
10.1145/3631991.3631998acmotherconferencesArticle/Chapter ViewAbstractPublication PageswsseConference Proceedingsconference-collections
research-article

Using Deep Learning and Object-Oriented Metrics to Identify Critical Components in Object-Oriented Systems

Published: 26 December 2023 Publication History

Abstract

This paper aims at studying the ability of deep machine learning to predict software faults based on object-oriented metrics. This research investigated software faults from the perspective of fault-proneness, faults number and faults frequency, and used data collected from several versions of a Java open-source software system. This study relied on Chidamber and Kemerer suite of metrics as proxy to capture various software characteristics. In this study, the deep learning regression and classification results were compared to linear and logistic regressions. Auto-Encoders (AE) and Principal Components Analysis (PCA) have been used to reduce redundant information from the dataset. To evaluate the prediction ability of the models, this research used the inter-version validation strategy. The results showed that the models can achieve a significant average performance up to 89%.

References

[1]
M. A. Jamil, M. Arif, N. S. A. Abubakar, and A. Ahmad, “Software Testing Techniques: A Literature Review,” presented at the 2016 6th International Conference on Information and Communication Technology for The Muslim World (ICT4M), Nov. 2016, pp. 177–182.
[2]
K. Aggarwal, Software engineering. New Age International, 2005.
[3]
B. Kitchenham and S. L. Pfleeger, “Software quality: the elusive target [special issues section],” IEEE software, vol. 13, no. 1, pp. 12–21, 1996.
[4]
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature, vol. 521, no. 7553, pp. 436–444, 2015.
[5]
S. Agatonovic-Kustrin and R. Beresford, “Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research,” Journal of pharmaceutical and biomedical analysis, vol. 22, no. 5, pp. 717–727, 2000.
[6]
M.-H. Tang, M.-H. Kao, and M.-H. Chen, “An empirical study on object-oriented metrics,” presented at the Proceedings sixth international software metrics symposium (Cat. No. PR00403), 1999, pp. 242–249.
[7]
S. R. Chidamber and C. F. Kemerer, “Towards a metrics suite for object oriented design,” presented at the Conference proceedings on Object-oriented programming systems, languages, and applications, 1991, pp. 197–211.
[8]
A. S. Foundation, “The Apache Software Foundation,” 1999, [Online]. Available: https://www.apache.org/
[9]
POI, “POI.” https://poi.apache.org/changes.html
[10]
S. S. Rathore and S. Kumar, “A study on software fault prediction techniques,” Artificial Intelligence Review, vol. 51, no. 2, pp. 255–327, Feb. 2019.
[11]
S. Moudache and M. Badri, “Software Fault Prediction Based on Fault Probability and Impact,” presented at the 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Dec. 2019, pp. 1178–1185.
[12]
S. S. Rathore and S. Kumar, “Linear and non-linear heterogeneous ensemble methods to predict the number of faults in software systems,” Knowledge-Based Systems, vol. 119, pp. 232–256, Mar. 2017.
[13]
L. Chen, “Empirical analysis of network measures for predicting high severity software faults,” Science China Information Sciences, vol. 59, no. 12, p. 122901, Nov. 2016.
[14]
D. Kumari and K. Rajnish, “A Systematic Approach Towards Development of Universal Software Fault Prediction Model Using Object-Oriented Design Measurement,” presented at the Nanoelectronics, Circuits and Communication Systems, Singapore, 2019, pp. 515–526.
[15]
M. Hamill and K. Goseva-Popstojanova, “Exploring fault types, detection activities, and failure severity in an evolving safety-critical software system,” Software Quality Journal, vol. 23, no. 2, pp. 229–265, Jun. 2015.
[16]
R. Jindal, R. Malhotra, and A. Jain, “Prediction of defect severity by mining software project reports,” International Journal of System Assurance Engineering and Management, vol. 8, no. 2, pp. 334–351, Jun. 2017.
[17]
A. Fernández, S. Garcia, F. Herrera, and N. V. Chawla, “SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary,” Journal of artificial intelligence research, vol. 61, pp. 863–905, 2018.
[18]
S. Moudache, “Prédiction du risque logiciel, une approche basée sur la probabilité et l'impact des fautes: évaluation empirique,” Université du Québec à Trois-Rivières, 2018.
[19]
J.Brains."Calculatemetrics. "https://plugins.jetbrains.com/plugin/93-metricsreloaded
[20]
Y. Shi, M. Lei, R. Ma, and L. Niu, "Learning Robust Auto-Encoders With Regularizer for Linearity and Sparsity," IEEE Access, vol. 7, pp. 17195-17206, 2019.
[21]
X. Su, X. Yan, and C. L. Tsai, "Linear regression," Wiley Interdisciplinary Reviews: Computational Statistics, vol. 4, no. 3, pp. 275-294, 2012.
[22]
Shweta Sharma and S. Srinivasan, "A Survey on Software Design Based and Project Based Metrics," International Journal of Computer Theory and Engineering vol. 14, no. 2, pp. 54-61, 2022.
[23]
O. Moravcik, D. Petrik, T. Skripcak, and P. Schreiber, "Elements of the Modern Application Software Development," International Journal of Computer Theory and Engineering vol. 4, no. 6, pp. 891-896, 2012.
[24]
S. Camiz and V. D. Pillar, "Identifying the informational/signal dimension in principal component analysis," Mathematics, vol. 6, no. 11, p. 269, 2018.
[25]
L. Ferré, "Selection of components in principal component analysis: a comparison of methods," Computational Statistics & Data Analysis, vol. 19, no. 6, pp. 669-682, 1995.

Index Terms

  1. Using Deep Learning and Object-Oriented Metrics to Identify Critical Components in Object-Oriented Systems
          Index terms have been assigned to the content through auto-classification.

          Recommendations

          Comments

          Information & Contributors

          Information

          Published In

          cover image ACM Other conferences
          WSSE '23: Proceedings of the 2023 5th World Symposium on Software Engineering
          September 2023
          352 pages
          ISBN:9798400708053
          DOI:10.1145/3631991
          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          Published: 26 December 2023

          Permissions

          Request permissions for this article.

          Check for updates

          Qualifiers

          • Research-article
          • Research
          • Refereed limited

          Conference

          WSSE 2023

          Contributors

          Other Metrics

          Bibliometrics & Citations

          Bibliometrics

          Article Metrics

          • 0
            Total Citations
          • 12
            Total Downloads
          • Downloads (Last 12 months)12
          • Downloads (Last 6 weeks)1
          Reflects downloads up to 26 Jan 2025

          Other Metrics

          Citations

          View Options

          Login options

          View options

          PDF

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader

          HTML Format

          View this article in HTML Format.

          HTML Format

          Figures

          Tables

          Media

          Share

          Share

          Share this Publication link

          Share on social media