We present a new system, Genesis, that processes human patches to automatically infer code transforms for automatic patch generation. We present results that characterize the effectiveness of the Genesis inference algorithms and the complete Genesis patch generation system working with real-world patches and defects collected from 372 Java projects. To the best of our knowledge, Genesis is the first system to automatically infer patch generation transforms or candidate patch search spaces from previous successful patches. Keywords: Patch generation; Code transform; Search space inferenceUnited States. Defense Advanced Research Projects Agency (Grant FA8750-14-2-0242
We analyze reported patches for three prior generate-and-validate patch generation systems (GenProg,...
We present the first systematic analysis of key characteristics of patch search spaces for automatic...
We analyze reported patches for three existing generate-and-validate patch generation systems (GenPr...
We present a new system, Genesis, that processes sets of human patches to automatically infer code t...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Comp...
We present Prophet, a novel patch generation system that learns a probabilistic model over candidate...
Recent work on genetic-programming-based approaches to auto-matic program patching have relied on th...
Recent work on genetic-programming-based approaches to auto-matic program patching have relied on th...
Patch generation is an essential software maintenance task because most software systems inevitably ...
We analyze reported patches for three prior generate-and-validate patch generation systems (GenProg,...
Dynamic analysis can identify improvements to programs that cannot feasibly be identified by static ...
We propose a tool for inferring transformation specifica-tions from a few examples of original and u...
In the field of automated program repair, the redundancy assumption claims large programs contain th...
Search-based program repair generates variants of a defective program to find its repair. This could...
International audienceIn the field of automated program repair, the redundancy assumption claims lar...
We analyze reported patches for three prior generate-and-validate patch generation systems (GenProg,...
We present the first systematic analysis of key characteristics of patch search spaces for automatic...
We analyze reported patches for three existing generate-and-validate patch generation systems (GenPr...
We present a new system, Genesis, that processes sets of human patches to automatically infer code t...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Comp...
We present Prophet, a novel patch generation system that learns a probabilistic model over candidate...
Recent work on genetic-programming-based approaches to auto-matic program patching have relied on th...
Recent work on genetic-programming-based approaches to auto-matic program patching have relied on th...
Patch generation is an essential software maintenance task because most software systems inevitably ...
We analyze reported patches for three prior generate-and-validate patch generation systems (GenProg,...
Dynamic analysis can identify improvements to programs that cannot feasibly be identified by static ...
We propose a tool for inferring transformation specifica-tions from a few examples of original and u...
In the field of automated program repair, the redundancy assumption claims large programs contain th...
Search-based program repair generates variants of a defective program to find its repair. This could...
International audienceIn the field of automated program repair, the redundancy assumption claims lar...
We analyze reported patches for three prior generate-and-validate patch generation systems (GenProg,...
We present the first systematic analysis of key characteristics of patch search spaces for automatic...
We analyze reported patches for three existing generate-and-validate patch generation systems (GenPr...