Using Screenshot Attachments in Issue Reports for Triaging

In previous work, we deployed IssueTAG, which uses the texts present in the one-line summary and the description fields of the issue reports to automatically assign them to the stakeholders, who are responsible for resolving the reported issues. Since its deployment on January 12, 2018 at Softtech, i.e., the software subsidiary of the largest private bank in Turkey, IssueTAG has made a total of 301,752 assignments (as of November 2021). One observation we make is that a large fraction of the issue reports submitted to Softtech has screenshot attachments and, in the presence of such attachments, the reports often convey less information in their one-line summary and the description fields, which tends to reduce the assignment accuracy. In this work, we use the screenshot attachments as an additional source of information to further improve the assignment accuracy, which (to the best of our knowledge) has not been studied before in this context. In particular, we develop a number of multi-source (using both the issue reports and the screenshot attachments) and single-source assignment models (using either the issue reports or the screenshot attachments) and empirically evaluate them on real issue reports. In the experiments, compared to the currently deployed single-source model in the field, the best multi-source model developed in this work, significantly (both in the practical and statistical sense) improved the assignment accuracy for the issue reports with screenshot attachments from 0.843 to 0.858 at acceptable overhead costs, a result strongly supporting our basic hypothesis.Comment: Preprint for EMSE journa

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

A Fine-grained Data Set and Analysis of Tangling in Bug Fixing Commits

Context: Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective: We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods: We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results: We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusion: Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise.Comment: Status: Accepted at Empirical Software Engineerin

Turkish issue report classification in banking domain [Bankacılık alanında Türkçe yazılım hata raporu sınıflandırması]

Users report the problems they encounter while using a software product with software issue reports. It is important that they are assigned to the correct software team or developer so that they are resolved quickly. Incorrect assignment may increase solution times, thus causing customer dissatisfaction. Past studies suggest to use text classification techniques to automatically assign issue reports. In this study, software issue reports written in Turkish, obtained from an industrial case in the banking sector are classified by applying deep learning techniques on word embedding representation, and the results are compared with our baseline model, which is applying Support Vector Machines (SVM) on top of the bag of words (BOW) model. In our study, best results are obtained when words are presented with BOW model and classes are predicted with the SVM algorithm

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

Olay kayıtları ürün ve platform kodu tespit süreci otomasyonu (The automation of the process of determining product and platform codes for issue records)

There are various platforms for tracking and solving issue records related to software products. It is an important operational issue in a large software company, to which team the record should be assigned on these platforms, which can lead to a waste of time in the solution of the record. The tracking of software products of Softtech A.S. are also done on such an issue tracking platform. The issues are entered by non-technical help-desk staff and forwarded to related software team. The help desk staff enter software product code and platform code for the records and this information is used in assigning the records to the related software teams. But the staff entering the issues don't have detailed information about the product and platform codes, and wrong entrance causes assigning the records to wrong teams. Since the record may circulate between different teams, this is thought to have an effect on the late solution of records. In this study, it's aimed to automate the process of assigning product and platform codes for issue records. As a result of comprehensive tests, 66% f-score and 67% accuracy were obtained

Factors affecting operative morbidity and long-term outcomes in patients undergoing surgery for presacral tumours: a multicentric cohort study from the Turkish Collaborative Group for Quality Improvement in Colorectal and Pelvic Surgery

Aim: Data regarding the operative management of presacral tumours present various dilemmas due to their rarity and heterogeneous nature. The aim of this study was to evaluate the management strategy, factors associated with operative morbidity and long-term postoperative outcomes in a large group of patients undergoing surgery for presacral tumours. Method: This study was designed as a multicentre retrospective cohort study. Records of patients who underwent surgery for presacral tumours at 10 tertiary colorectal centres between 1996 and 2017 were evaluated. Results: One hundred and twenty seven patients (44 men) with a mean age of 46 years and body mass index of 27 kg/m2 were included. Fifty eight per cent of the patients had low sacral lesions (below S3). The operative approaches were transabdominal (17%), transsacral (65%) and abdominosacral (17%). The postoperative morbidity was 19%. Thirty per cent of the patients had a malignant tumour. Longer duration of symptoms (p = 0.001), higher American Society of Anesthesiologists score (p = 0.01), abdominosacral operations (p = 0.0001) and presacral tumours located above S3 (p = 0.004) were associated with an increased risk of postoperative morbidity. Overall long-term postoperative recurrence and mortality were 6% and 5%, respectively, within a 3-year mean follow-up period in patients with presacral malignant tumours. Conclusion: Reduced physical condition, omission of symptoms prior to surgery, combined resections and high sacral tumours are the risk factors associated with postoperative complications in patients undergoing surgery for presacral tumours. Meticulous planning of the operation and intensified perioperative care may improve the outcomes in high-risk patients