Learning to rank developers for bug report assignment

Alkhazi, Bader; DiStasi, Andrew; Aljedaani, Wajdi; Alrubaye, Hussein; Ye, Xin; Mkaouer, Mohamed Wiem

doi:10.1016/j.asoc.2020.106667

Cited by 25 publications

(7 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, Alkhazi et al. [21] extract information from commit messages and treat it as domain knowledge to create developer profiling features, which are thereafter used to determine the most suitable developers for resolving a bug report. An interesting alternative is proposed by Sajedi‐Badashian and Stroulia [22].…”

Section: Related Workmentioning

confidence: 99%

“…To do so, they use a dataset extracted from GitHub [20] and employ a neural network that aggregates features probabilities in order to automate the assignment of GitHub issues. Similarly, Alkhazi et al [21] extract information from commit messages and treat it as domain knowledge to create developer profiling features, which are thereafter used to determine the most suitable developers for resolving a bug report. An interesting alternative is proposed by Sajedi-Badashian and Stroulia [22].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Automated issue assignment using topic modelling on Jira issue tracking data

Diamantopoulos,

Saoulidis,

Symeonidis

2023

IET Software

View full text Add to dashboard Cite

As more and more software teams use online issue tracking systems to collaborate on software projects, the accurate assignment of new issues to the most suitable contributors may have significant impact on the success of the project. As a result, several research efforts have been directed towards automating this process to save considerable time and effort. However, most approaches focus mainly on software bugs and employ models that do not sufficiently take into account the semantics and the non-textual metadata of issues and/or produce models that may require manual tuning. A methodology that extracts both textual and non-textual features from different types of issues is designed, providing a Jira dataset that involves not only bugs but also new features, issues related to documentation, patches, etc. Moreover, the semantics of issue text are effectively captured by employing a topic modelling technique that is optimised using the assignment result. Finally, this methodology aggregates probabilities from a set of individual models to provide the final assignment. Upon evaluating this approach in an automated issue assignment setting using a dataset of Jira issues, the authors conclude that it can be effective for automated issue assignment.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Automated issue assignment using topic modelling on Jira issue tracking data

Diamantopoulos,

Saoulidis,

Symeonidis

2023

IET Software

View full text Add to dashboard Cite

show abstract

“…Several studies on software engineering have demonstrated that the deadline pressure and the developer's inexperience are among the factors that may lead to the introduction of the so-called technical debt [1,2], which refers to a group of design issues that may affect the software's maintenance as well as its evolution in the future [3,4]. Code smells (a.k.a.…”

Section: Introductionmentioning

confidence: 99%

“…Motivated by this observation, researchers have paid attention to code smell detection [10] and they have proposed different techniques to automatically detect code smells within software systems codebases [4]. These techniques could be classified into three categories: (1) rule/heuristic-based approaches [11], (2) machine learning-based approaches [12], and (3) search-based ones [13]. Mantyla et al have been discussing the uncertainty issue as one of the major individual human factors that may influence software engineers' decisions about the smelliness of software classes for more than fifteen years, and more specifically since 2004.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty-wise software anti-patterns detection: A possibilistic evolutionary machine learning approach

Boutaib

Elarbi

Bechikh

et al. 2022

Applied Soft Computing

View full text Add to dashboard Cite

“…Many studies classify app reviews using different taxonomies [91,105,148,203,241,245,24,26,37,25,173], for various purposes: detection of potential feature requests, bug reports, complaints, and praises, etc. Even though many of them identify reviews related to app usability, there is no explicit mention of accessibility-related issues [112].…”

Section: Classification Of Text Documentsmentioning

confidence: 99%

Toward Leveraging Artificial Intelligence to Support the Identification of Accessibility Challenges

Aljedaani

View full text Add to dashboard Cite

The goal of this thesis is to support the automated identification of accessibility in user reviews or bug reports, to help technology professionals prioritize their handling, and, thus, to create more inclusive apps. Particularly, we propose a model that takes as input accessibility user reviews or bug reports and learns their keyword-based features to make a classification decision, for a given review, on whether it is about accessibility or not. Our empirically driven study follows a mixture of qualitative and quantitative methods. We introduced models that can accurately identify accessibility reviews and bug reports and automate detecting them. Our models can automatically classify app reviews and bug reports as accessibility-related or not so developers can easily detect accessibility issues with their products and improve them to more accessible and inclusive apps utilizing the users' input. Our goal is to create a sustainable change by including a model in the developer's software maintenance pipeline and raising awareness of existing errors that hinder the accessibility of mobile apps, which is a pressing need. In light of our findings from the Blackboard case study, Blackboard and the course material are not easily accessible to deaf students and hard of hearing. Thus, deaf students find that learning is extremely stressful during the pandemic.

show abstract

Learning to rank developers for bug report assignment

Cited by 25 publications

References 13 publications

Automated issue assignment using topic modelling on Jira issue tracking data

Automated issue assignment using topic modelling on Jira issue tracking data

Uncertainty-wise software anti-patterns detection: A possibilistic evolutionary machine learning approach

Toward Leveraging Artificial Intelligence to Support the Identification of Accessibility Challenges

Contact Info

Product

Resources

About