Browsing by Subject "Software bug report classification"

Now showing 1 - 3 of 3

Open Access
Software module classification for commercial bug reports
(Institute of Electrical and Electronics Engineers, 2023-08-02) Öztürk, Ceyhun Emre; Yilmaz, E. H.; Koksal, O.; Koç, Aykut
In this work, we curate and investigate a dataset named Turkish Software Report - Module Classification (TSRMC), consisting of commercial software bug reports of a company. Automated bug classification is required in large-scale software projects due to the vast amount of bugs. We analyze and report the statistical features and classification difficulty of the dataset. We use several methods from the text classification literature to assign each bug report of the TSRMC dataset a suitable software module. The utilized methods include traditional machine learning (ML) methods, such as support vector machine (SVM) and logistic regression; sequential deep learning (DL) models, such as gated recurrent unit (GRU) and convolutional neural networks (CNN); and Bidirectional Encoder Representations from Transformers (BERT)-based pre-trained language models (PLMs). Our work is one of the first efforts in automated bug report classification literature that focuses on commercial bugs and uses bilingual (Turkish and English) texts.
Open Access
A survey on machine learning-based automated software bug report classification
(Institute of Electrical and Electronics Engineers, 2022-11-14) Köksal, Ö.; Öztürk, Ceyhun Emre
In software development processes, classifying software bugs is a vital step since it helps grasp the nature, implications, and causes of software failures. Further, categorization enables reacting to software bugs appropriately and faster. However, manual classification of software bugs is inefficient and costly, especially in large-scale software projects, since one must deal with extensive bug reports from multiple sources. Hence, many studies have addressed this problem by automated software bug classification with the help of machine learning techniques. Researchers used various machine learning-based algorithms and techniques to obtain better classification performance. Furthermore, many researchers used open source bug repositories to compare their results with previous studies. In this paper, we aimed to report the main studies in machine learning-based automated software bug report classification by highlighting the recent improvements and indicating the key steps in this process. So, this survey can benefit the researchers and practitioners working in automated software bug report classification and other related domains.
Open Access
Transformer-based bug/feature classification
(IEEE - Institute of Electrical and Electronics Engineers, 2023-08-28) Öztürk, Ceyhun Emre; Yılmaz, E. H.; Köksal, Ö.
Automatic classification of a software bug report as a 'bug' or 'feature' is essential to accelerate closed-source software development. In this work, we focus on automating the bug/feature classification task with artificial intelligence using a newly constructed dataset of Turkish software bug reports collected from a commercial project. We train and test support vector machine (SVM), k-nearest neighbors (KNN), convolutional neural network (CNN), transformer-based models, and similar artificial intelligence models on the collected reports. Results of the experiments show that transformer-based BERTurk is the best-performing model for the bug/feature classification task.