no code implementations • 7 Apr 2024 • Michael Fu, Jirat Pasuksmit, Chakkrit Tantithamthavorn
Drawing insights from our findings, we discussed the state-of-the-art AI-driven security approaches, highlighted challenges in existing research, and proposed avenues for future opportunities.
no code implementations • 21 Mar 2024 • Aastha Pant, Rashina Hoda, Chakkrit Tantithamthavorn, Burak Turhan
We conducted semi-structured interviews with 22 AI practitioners to investigate their understanding of what a 'fair AI/ML' is, the challenges they face in developing a fair AI/ML, the consequences of developing an unfair AI/ML, and the strategies they employ to ensure AI/ML fairness.
no code implementations • 15 Feb 2024 • Yang Hong, Chakkrit Tantithamthavorn, Jirat Pasuksmit, Patanamon Thongtanunam, Arik Friedman, Xing Zhao, Anton Krasikov
Continuous Integration (CI) build failures could significantly impact the software development process and teams, such as delaying the release of new features and reducing developers' productivity.
1 code implementation • 27 Oct 2023 • Xinyu She, Yue Liu, Yanjie Zhao, Yiling He, Li Li, Chakkrit Tantithamthavorn, Zhan Qin, Haoyu Wang
After carefully examining these studies, we designed a taxonomy of pitfalls in LM4Code research and conducted a systematic study to summarize the issues, implications, current solutions, and challenges of different pitfalls for LM4Code systems.
no code implementations • 14 Jul 2023 • Aastha Pant, Rashina Hoda, Simone V. Spiegler, Chakkrit Tantithamthavorn, Burak Turhan
But what do people who build AI - AI practitioners - have to say about their understanding of AI ethics and the challenges associated with incorporating it in the AI-based systems they develop?
1 code implementation • 26 May 2023 • Michael Fu, Trung Le, Van Nguyen, Chakkrit Tantithamthavorn, Dinh Phung
Prior studies found that vulnerabilities across different vulnerable programs may exhibit similar vulnerable scopes, implicitly forming discernible vulnerability patterns that can be learned by DL models through supervised training.
1 code implementation • 9 Nov 2022 • Wannita Takerngsaksiri, Chakkrit Tantithamthavorn, Yuan-Fang Li
However, existing syntax-aware code completion approaches are not on-the-fly, as we found that for every two-thirds of characters that developers type, AST fails to be extracted because it requires the syntactically correct source code, limiting its practicality in real-world scenarios.
1 code implementation • 20 Sep 2022 • Van Nguyen, Trung Le, Chakkrit Tantithamthavorn, John Grundy, Hung Nguyen, Seyit Camtepe, Paul Quirk, Dinh Phung
In this paper we propose a novel end-to-end deep learning-based approach to identify the vulnerability-relevant code statements of a specific function.
1 code implementation • 19 Sep 2022 • Van Nguyen, Trung Le, Chakkrit Tantithamthavorn, John Grundy, Hung Nguyen, Dinh Phung
However, there are still two open and significant issues for SVD in terms of i) learning automatic representations to improve the predictive performance of SVD, and ii) tackling the scarcity of labeled vulnerabilities datasets that conventionally need laborious labeling effort by experts.
1 code implementation • 9 Mar 2021 • Yue Liu, Chakkrit Tantithamthavorn, Li Li, Yepang Liu
In this paper, we conducted a systematic literature review to search and analyze how deep learning approaches have been applied in the context of malware defenses in the Android environment.
1 code implementation • 19 Feb 2021 • Dilini Rajapaksha, Chakkrit Tantithamthavorn, Jirayus Jiarpakdee, Christoph Bergmeir, John Grundy, Wray Buntine
Thus, our SQAPlanner paves a way for novel research in actionable software analytics-i. e., generating actionable guidance on what should practitioners do and not do to decrease the risk of having defects to support SQA planning.
no code implementations • 3 Dec 2020 • Chakkrit Tantithamthavorn, Jirayus Jiarpakdee, John Grundy
Artificial Intelligence/Machine Learning techniques have been widely used in software engineering to improve developer productivity, the quality of software systems, and decision-making.
no code implementations • 26 Jun 2018 • Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Christoph Treude
Through a case study of 13 publicly-available defect datasets, we find that feature selection techniques produce inconsistent subsets of metrics and do not mitigate correlated metrics, suggesting that feature selection techniques should not be used and correlation analyses must be applied when the goal is model interpretation.