Defect prediction with bad smells in code

Background: Defect prediction in software can be highly beneficial for development projects, when prediction is highly effective and defect-prone areas are predicted correctly. One of the key elements to gain effective software defect prediction is proper selection of metrics used for dataset preparation. Objective: The purpose of this research is to verify, whether code smells metrics, collected using Microsoft CodeAnalysis tool, added to basic metric set, can improve defect prediction in industrial software development project. Results: We verified, if dataset extension by the code smells sourced metrics, change the effectiveness of the defect prediction by comparing prediction results for datasets with and without code smells-oriented metrics. In a result, we observed only small improvement of effectiveness of defect prediction when dataset extended with bad smells metrics was used: average accuracy value increased by 0.0091 and stayed within the margin of error. However, when only use of code smells based metrics were used for prediction (without basic set of metrics), such process resulted with surprisingly high accuracy (0.8249) and F-measure (0.8286) results. We also elaborated data anomalies and problems we observed when two different metric sources were used to prepare one, consistent set of data. Conclusion: Extending the dataset by the code smells sourced metric does not significantly improve the prediction effectiveness. Achieved result did not compensate effort needed to collect additional metrics. However, we observed that defect prediction based on the code smells only is still highly effective and can be used especially where other metrics hardly be used.Comment: Chapter 10 in Software Engineering: Improving Practice through Research (B. Hnatkowska and M. \'Smia{\l}ek, eds.), pp. 163-176, 201

Too Trivial To Test? An Inverse View on Defect Prediction to Identify Methods with Low Fault Risk

Background. Test resources are usually limited and therefore it is often not possible to completely test an application before a release. To cope with the problem of scarce resources, development teams can apply defect prediction to identify fault-prone code regions. However, defect prediction tends to low precision in cross-project prediction scenarios. Aims. We take an inverse view on defect prediction and aim to identify methods that can be deferred when testing because they contain hardly any faults due to their code being "trivial". We expect that characteristics of such methods might be project-independent, so that our approach could improve cross-project predictions. Method. We compute code metrics and apply association rule mining to create rules for identifying methods with low fault risk. We conduct an empirical study to assess our approach with six Java open-source projects containing precise fault data at the method level. Results. Our results show that inverse defect prediction can identify approx. 32-44% of the methods of a project to have a low fault risk; on average, they are about six times less likely to contain a fault than other methods. In cross-project predictions with larger, more diversified training sets, identified methods are even eleven times less likely to contain a fault. Conclusions. Inverse defect prediction supports the efficient allocation of test resources by identifying methods that can be treated with less priority in testing activities and is well applicable in cross-project prediction scenarios.Comment: Submitted to PeerJ C

Connecting Software Metrics across Versions to Predict Defects

Accurate software defect prediction could help software practitioners allocate test resources to defect-prone modules effectively and efficiently. In the last decades, much effort has been devoted to build accurate defect prediction models, including developing quality defect predictors and modeling techniques. However, current widely used defect predictors such as code metrics and process metrics could not well describe how software modules change over the project evolution, which we believe is important for defect prediction. In order to deal with this problem, in this paper, we propose to use the Historical Version Sequence of Metrics (HVSM) in continuous software versions as defect predictors. Furthermore, we leverage Recurrent Neural Network (RNN), a popular modeling technique, to take HVSM as the input to build software prediction models. The experimental results show that, in most cases, the proposed HVSM-based RNN model has a significantly better effort-aware ranking effectiveness than the commonly used baseline models

Continuous Defect Prediction: The Idea and a Related Dataset

We would like to present the idea of our Continuous Defect Prediction (CDP) research and a related dataset that we created and share. Our dataset is currently a set of more than 11 million data rows, representing files involved in Continuous Integration (CI) builds, that synthesize the results of CI builds with data we mine from software repositories. Our dataset embraces 1265 software projects, 30,022 distinct commit authors and several software process metrics that in earlier research appeared to be useful in software defect prediction. In this particular dataset we use TravisTorrent as the source of CI data. TravisTorrent synthesizes commit level information from the Travis CI server and GitHub open-source projects repositories. We extend this data to a file change level and calculate the software process metrics that may be used, for example, as features to predict risky software changes that could break the build if committed to a repository with CI enabled.Comment: Lech Madeyski and Marcin Kawalerowicz. "Continuous Defect Prediction: The Idea and a Related Dataset" In: 14th International Conference on Mining Software Repositories (MSR'17). Buenos Aires. 2017, pp. 515-518. doi: 10.1109/MSR.2017.46. URL: http://madeyski.e-informatyka.pl/download/MadeyskiKawalerowiczMSR17.pd

Deep Learning for Software Defect Prediction: An LSTM-based Approach

Software defect prediction is an important aspect of software development, as it helps developers and organizations to identify and resolve bugs in the software before they become major issues. In this paper, we explore the use of machine learning algorithms for software defect prediction. We discuss the different types of machine learning algorithms that have been used for software defect prediction and their advantages and disadvantages. We also provide a comprehensive review of recent studies that have used machine learning algorithms for software defect prediction. The paper concludes with a discussion of the challenges and opportunities in using machine learning algorithms for software defect prediction and the future directions of research in this field. This paper surveys the existing literature on software defect prediction, focusing specifically on deep learning techniques. Compared to existing surveys on the topic, this paper offers a more in-depth analysis of the strengths and weaknesses of deep learning approaches for software defect prediction. It explores the use of LSTMs for this task, which have not been extensively studied in previous surveys. Additionally, this paper provides a comprehensive review of recent research in the field, highlighting the most promising deep learning models and techniques for software defect prediction. The results of this survey demonstrate that LSTM-based deep learning models can outperform traditional machine learning approaches and achieve state-of-the-art results in software defect prediction. Furthermore, this paper provides insights into the challenges and limitations of deep learning approaches for software defect prediction, highlighting areas for future research and improvement. Overall, this paper offers a valuable resource for researchers and practitioners interested in using deep learning techniques for software defect prediction.