Bug Prediction Using Source Code Embedding Based on Doc2Vec
Pages 382 - 397
Abstract
Bug prediction is a resource demanding task that is hard to automate using static source code analysis. In many fields of computer science, machine learning has proven to be extremely useful in tasks like this, however, for it to work we need a way to use source code as input. We propose a simple, but meaningful representation for source code based on its abstract syntax tree and the Doc2Vec embedding algorithm. This representation maps the source code to a fixed length vector which can be used for various upstream tasks – one of which is bug prediction. We measured this approach’s validity by itself and its effectiveness compared to bug prediction based solely on code metrics. We also experimented on numerous machine learning approaches to check the connection between different embedding parameters with different machine learning models. Our results show that this representation provides meaningful information as it improves the bug prediction accuracy in most cases, and is always at least as good as only using code metrics as features.
References
[1]
Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016, pp. 265–283 (2016)
[2]
OpenStaticAnalyzer (2021). https://github.com/sed-inf-u-szeged/OpenStaticAnalyzer
[3]
Chen, Z., Monperrus, M.: A literature study of embeddings on source code (2019)
[4]
D’Ambros, M., Lanza, M., Robbes, R.: An extensive comparison of bug prediction approaches. In: 2010 7th IEEE Working Conference on Mining Software Repositories. MSR 2010, pp. 31–41 (2010)
[5]
DeFreez, D., Thakur, A.V., Rubio-González, C.: Path-based function embedding and its application to specification mining. CoRR, abs/1802.07779 (2018)
[6]
Devlin, J., Uesato, J., Singh, R., Kohli, P.: Semantic code repair using neuro-symbolic transformation networks. CoRR, abs/1710.11054 (2017)
[7]
Ferenc, R., Bán, D., Grósz, T., Gyimóthy, T.: Deep learning in static, metric-based bug prediction. Array, 6:100021. Open Access (2020a)
[8]
Ferenc, R., Tóth, Z., Ladányi, G., Siket, I., Gyimóthy, T.: A public unified bug dataset for java and its assessment regarding metrics and bug prediction. Softw. Qual. J. 28, 1447–1506 (2020b). Open Access
[9]
Ferenc, R., Viszkok, T., Aladics, T., Jász, J., Hegedűs, P.: Deep-water framework: the Swiss army knife of humans working with machine learning models. SoftwareX 12, 100551 (2020c). Open Access
[10]
Hammouri A, Hammad M, Alnabhan M, and Alsarayrah F Software bug prediction using machine learning approach Int. J. Adv. Comput. Sci. Appl. 2018 9 2 78-83
[11]
Harer, J., et al.: Automated software vulnerability detection with machine learning (2018)
[12]
Jureczko, M., Madeyski, L.: Towards identifying software project clusters with regard to defect prediction. In: Proceedings of the 6th International Conference on Predictive Models in Software Engineering, PROMISE 2010. Association for Computing Machinery, New York, NY, USA (2010)
[13]
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013a)
[14]
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality (2013b)
[15]
Narayanan, A., Chandramohan, M., Venkatesan, R., Chen, L., Liu, Y., Jaiswal, S.: graph2vec: learning distributed representations of graphs (2017)
[16]
Pan C, Lu M, Xu B, and Gao H An improved CNN model for within-project software defect prediction Appl. Sci. 2019 9 10 2138
[17]
Pedregosa F et al. Scikit-learn: machine learning in Python J. Mach. Learn. Res. 2011 12 2825-2830
[18]
Puranik, S., Deshpande, P., Chandrasekaran, K.: A novel machine learning approach for bug prediction. Procedia Comput. Sci. 93, 924–930 (2016). Proceedings of the 6th International Conference on Advances in Computing and Communications
[19]
Shippey T, Bowes D, and Hall T Automatically identifying code features for software defect prediction: using AST N-grams Inf. Softw. Technol. 2019 106 142-160
[20]
Tóth Z, Gyimesi P, Ferenc R, et al. Gervasi O et al. A public bug database of GitHub projects and its application in bug prediction Computational Science and Its Applications – ICCSA 2016 2016 Cham Springer 625-638
[21]
Wang, S., Liu, T., Tan, L.: Automatically learning semantic features for defect prediction. In: 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE), pp. 297–308 (2016)
Index Terms
- Bug Prediction Using Source Code Embedding Based on Doc2Vec
Index terms have been assigned to the content through auto-classification.
Recommendations
Bug Prediction Capability of Primitive Enthusiasm Metrics
Computational Science and Its Applications – ICCSA 2021AbstractBugs in software development life cycle are unavoidable. Manually finding these bugs is not always the most effective way. To aid this, various bug prediction approaches which are using code metrics are developed and are also still in active ...
A public unified bug dataset for java and its assessment regarding metrics and bug prediction
AbstractBug datasets have been created and used by many researchers to build and validate novel bug prediction models. In this work, our aim is to collect existing public source code metric-based bug datasets and unify their contents. Furthermore, we wish ...
Method-level bug prediction
ESEM '12: Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurementResearchers proposed a wide range of approaches to build effective bug prediction models that take into account multiple aspects of the software development process. Such models achieved good prediction performance, guiding developers towards those ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Sep 2021
747 pages
ISBN:978-3-030-87006-5
DOI:10.1007/978-3-030-87007-2
© Springer Nature Switzerland AG 2021.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 13 September 2021
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 01 Jan 2025