Issue Report Validation in an Industrial Context

Effective issue triaging is crucial for software development teams to improve software quality, and thus customer satisfaction. Validating issue reports manually can be time-consuming, hindering the overall efficiency of the triaging process. This paper presents an approach on automating the validation of issue reports to accelerate the issue triaging process in an industrial set-up. We work on 1,200 randomly selected issue reports in banking domain, written in Turkish, an agglutinative language, meaning that new words can be formed with linear concatenation of suffixes to express entire sentences. We manually label these reports for validity, and extract the relevant patterns indicating that they are invalid. Since the issue reports we work on are written in an agglutinative language, we use morphological analysis to extract the features. Using the proposed feature extractors, we utilize a machine learning based approach to predict the issue reports' validity, performing a 0.77 F1-score.Comment: Accepted for publication in Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE'23

Improving the quality of software issue report descriptions in Turkish: an industrial case study at Softtech

Issue reports are an important part of the software development process. They help developers identify and fix problems in their code. However, problems described in these reports often lack important information, such as the Observed Behavior (OB), Expected Behavior (EB), and Steps to Reproduce (S2R). This can lead to valuable developer time being wasted on gathering the relevant information. This study aims to address this issue by developing a tool that guides reporters in providing the necessary information in an industrial setting. The study is conducted at Softtech, a software subsidiary of the largest private bank in Turkey. The proposed approach is developed for issue reports written specifically in Turkish language. It is motivated by the need for issue report classification tools that can handle the unique characteristics of the Turkish language, such as the presence of many compound words. We first manually analyze and label 1, 041 issue reports for the existence of OB, S2R, and EB, and then present the specific patterns we found describing the related information. Next, we use morphological analysis to extract keywords and suffixes, and then use them for classification with a machine learning based approach. In addition, we conduct a feasibility study to assess the potential of using large language models for issue report classification tasks as a direction for future research. The results indicate that the tool using the machine learning-based approach can be used to guide in improving the quality of issue reports at Softtech, thereby saving valuable developer time