Machine/deep learning for software engineering: A systematic literature review

S Wang, L Huang, A Gao, J Ge, T Zhang… - IEEE Transactions …, 2022‏ - ieeexplore.ieee.org
Since 2009, the deep learning revolution, which was triggered by the introduction of
ImageNet, has stimulated the synergy between Software Engineering (SE) and Machine …

Swe-bench: Can language models resolve real-world github issues?

CE Jimenez, J Yang, A Wettig, S Yao, K Pei… - arxiv preprint arxiv …, 2023‏ - arxiv.org
Language models have outpaced our ability to evaluate them effectively, but for their future
development it is essential to study the frontier of their capabilities. We find real-world …

[HTML][HTML] Industrial applications of software defect prediction using machine learning: A business-driven systematic literature review

S Stradowski, L Madeyski - Information and Software Technology, 2023‏ - Elsevier
Context: Machine learning software defect prediction is a promising field of software
engineering, attracting a great deal of attention from the research community; however, its …

Securing the ethereum from smart ponzi schemes: Identification using static features

Z Zheng, W Chen, Z Zhong, Z Chen, Y Lu - ACM Transactions on …, 2023‏ - dl.acm.org
Malware detection approaches have been extensively studied for traditional software
systems. However, the development of blockchain technology has promoted the birth of a …

Marscode agent: Ai-native automated bug fixing

Y Liu, P Gao, X Wang, J Liu, Y Shi, Z Zhang… - arxiv preprint arxiv …, 2024‏ - arxiv.org
Recent advances in large language models (LLMs) have shown significant potential to
automate various software development tasks, including code completion, test generation …

Automated classification of overfitting patches with statically extracted code features

H Ye, J Gu, M Martinez, T Durieux… - IEEE Transactions on …, 2021‏ - ieeexplore.ieee.org
Automatic program repair (APR) aims to reduce the cost of manually fixing software defects.
However, APR suffers from generating a multitude of overfitting patches, those patches that …

Rlocator: Reinforcement learning for bug localization

P Chakraborty, M Alfadel… - IEEE Transactions on …, 2024‏ - ieeexplore.ieee.org
Software developers spend a significant portion of time fixing bugs in their projects. To
streamline this process, bug localization approaches have been proposed to identify the …

Can higher-order mutants improve the performance of mutation-based fault localization?

H Wang, Z Li, Y Liu, X Chen, D Paul… - IEEE Transactions on …, 2022‏ - ieeexplore.ieee.org
First-order mutants (FOMs) have been widely used in mutation-based fault localization
(MBFL) approaches and have achieved promising results in single-fault localization …

Software fault localization: An overview of research, techniques, and tools

WE Wong, R Gao, Y Li, R Abreu… - Handbook of Software …, 2023‏ - Wiley Online Library
This chapter describes traditional and intuitive fault localization techniques, including
program logging, assertions, breakpoints, and profiling. Many advanced fault localization …

POWER: Program option-aware fuzzer for high bug detection ability

A Lee, I Ariq, Y Kim, M Kim - 2022 IEEE Conference on …, 2022‏ - ieeexplore.ieee.org
Most programs with command-line interface (CLI) have dozens of command-line options
(eg,-l,-F,-R for ls) to alternate the operation of the programs. Thus, depending on the option …