Zobrazeno 1 - 10
of 75
pro vyhledávání: '"Tahir, Amjed"'
Autor:
Nakao, Masato, Hamamoto, Kensei, Tsunoda, Masateru, Tahir, Amjed, Toda, Koji, Monden, Akito, Nakasai, Keitaro, Matsumoto, Kenichi
Developers must select a high-performance fault localization (FL) technique from available ones. A conventional approach is to try to select only one FL technique that is expected to attain high performance before debugging activity. In contrast, we
Externí odkaz:
http://arxiv.org/abs/2409.06268
Autor:
Hamamoto, Kensei, Tsunoda, Masateru, Tahir, Amjed, Bennin, Kwabena Ebo, Monden, Akito, Toda, Koji, Nakasai, Keitaro, Matsumoto, Kenichi
Ensemble learning methods have been used to enhance the reliability of defect prediction models. However, there is an inconclusive stability of a single method attaining the highest accuracy among various software projects. This work aims to improve
Externí odkaz:
http://arxiv.org/abs/2409.06264
Autor:
Murakami, Yukasa, Yamasaki, Yuta, Tsunoda, Masateru, Monden, Akito, Tahir, Amjed, Bennin, Kwabena Ebo, Toda, Koji, Nakasai, Keitaro
Cross-project defect prediction (CPDP) aims to use data from external projects as historical data may not be available from the same project. In CPDP, deciding on a particular historical project to build a training model can be difficult. To help wit
Externí odkaz:
http://arxiv.org/abs/2404.11040
Autor:
Fedorov, Nikolay, Yamasaki, Yuta, Tsunoda, Masateru, Monden, Akito, Tahir, Amjed, Bennin, Kwabena Ebo, Toda, Koji, Nakasai, Keitaro
Building defect prediction models based on online learning can enhance prediction accuracy. It continuously rebuilds a new prediction model, when a new data point is added. However, a module predicted as "non-defective" can result in fewer test cases
Externí odkaz:
http://arxiv.org/abs/2404.11033
Managing data related to a software product and its development poses significant challenges for software projects and agile development teams. Challenges include integrating data from diverse sources and ensuring data quality in light of continuous
Externí odkaz:
http://arxiv.org/abs/2402.00462
Autor:
Yu, Jiaxin, Liang, Peng, Fu, Yujia, Tahir, Amjed, Shahin, Mojtaba, Wang, Chong, Cai, Yangxiao
Security code review, as a time-consuming and labour-intensive process, typically requires integration with automated security defect detection tools to ensure code security. Despite the emergence of numerous security analysis tools, those tools face
Externí odkaz:
http://arxiv.org/abs/2401.16310
Autor:
Shima, Ryoto, Tsunoda, Masateru, Murakami, Yukasa, Monden, Akito, Tahir, Amjed, Bennin, Kwabena Ebo, Toda, Koji, Nakasai, Keitaro
Background: Recently, code generation tools such as ChatGPT have drawn attention to their performance. Generally, a prior analysis of their performance is needed to select new code-generation tools from a list of candidates. Without such analysis, th
Externí odkaz:
http://arxiv.org/abs/2312.12813
Autor:
Aktar, Mst Shamima, Liang, Peng, Waseem, Muhammad, Tahir, Amjed, Ahmad, Aakash, Zhang, Beiqi, Li, Zengyang
Quantum computing provides a new dimension in computation, utilizing the principles of quantum mechanics to potentially solve complex problems that are currently intractable for classical computers. However, little research has been conducted about t
Externí odkaz:
http://arxiv.org/abs/2312.05421
Autor:
Majdinasab, Vahid, Bishop, Michael Joshua, Rasheed, Shawn, Moradidakhel, Arghavan, Tahir, Amjed, Khomh, Foutse
AI-powered code generation models have been developing rapidly, allowing developers to expedite code generation and thus improve their productivity. These models are trained on large corpora of code (primarily sourced from public repositories), which
Externí odkaz:
http://arxiv.org/abs/2311.11177
Modern code generation tools, utilizing AI models like Large Language Models (LLMs), have gained popularity for producing functional code. However, their usage presents security challenges, often resulting in insecure code merging into the code base.
Externí odkaz:
http://arxiv.org/abs/2310.02059