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Machine/deep learning for software engineering: A systematic literature review
Since 2009, the deep learning revolution, which was triggered by the introduction of
ImageNet, has stimulated the synergy between Software Engineering (SE) and Machine …
ImageNet, has stimulated the synergy between Software Engineering (SE) and Machine …
Swe-bench: Can language models resolve real-world github issues?
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 …
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
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 …
engineering, attracting a great deal of attention from the research community; however, its …
Securing the ethereum from smart ponzi schemes: Identification using static features
Malware detection approaches have been extensively studied for traditional software
systems. However, the development of blockchain technology has promoted the birth of a …
systems. However, the development of blockchain technology has promoted the birth of a …
Marscode agent: Ai-native automated bug fixing
Recent advances in large language models (LLMs) have shown significant potential to
automate various software development tasks, including code completion, test generation …
automate various software development tasks, including code completion, test generation …
Automated classification of overfitting patches with statically extracted code features
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 …
However, APR suffers from generating a multitude of overfitting patches, those patches that …
Rlocator: Reinforcement learning for bug localization
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 …
streamline this process, bug localization approaches have been proposed to identify the …
Can higher-order mutants improve the performance of mutation-based fault localization?
First-order mutants (FOMs) have been widely used in mutation-based fault localization
(MBFL) approaches and have achieved promising results in single-fault localization …
(MBFL) approaches and have achieved promising results in single-fault localization …
Software fault localization: An overview of research, techniques, and tools
This chapter describes traditional and intuitive fault localization techniques, including
program logging, assertions, breakpoints, and profiling. Many advanced fault localization …
program logging, assertions, breakpoints, and profiling. Many advanced fault localization …
POWER: Program option-aware fuzzer for high bug detection ability
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 …
(eg,-l,-F,-R for ls) to alternate the operation of the programs. Thus, depending on the option …