Automatic error elimination by horizontal code transfer across multiple applications

Sidiroglou-DouskosStelios,; LahtinenEric,; LongFan,; RinardMartin,

doi:10.1145/2813885.2737988

Cited by 35 publications

(14 citation statements)

References 45 publications

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“…Some tools such as GenProg [40] and RSRepair [41] transplanted code to other places in the same system for eliminating bugs. A tool named CodePhage [42] aimed at automatically transferring correct code from donor applications into recipient applications but it was designed for only transferring checks between applications that process the same inputs. On top of RSRepair, SCRepair [43] proposed reusability metrics of similar code fragments and the transferring process was guided by the reusability values.…”

Section: B Code Transplantationmentioning

confidence: 99%

Dissection on Java Organs in GitHub Repositories

Wang¹,

Mao²,

Yi³

2019

IEEE Access

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Organ transplantation has brought convenience for software reuse and evolution since it was proposed. However, studies about mature, high-quality organs are still insufficient. It is still unclear about the detailed characteristics of organs in the open-source environment. In this paper, we look deep into organs obtained from software evolution processes of the ten large-scale Java repositories hosted on GitHub, aiming at providing practical information for utilizing organs in the open-source environment. We found that: 1) commits use add as a keyword in their comments possess the most organs, occupying 38% of the total amount, but commits with the keyword fix possess the highest locating accuracy (about 57%); 2) developers prefer to add new classes when they bring new functionalities to the projects in that the proportion of class level organs is 40%, more than statement level organs' and function level organs' (35% and 25%, respectively); 3) nearly 70% of the total amount are cross-file organs with the median of the number of files each organ spans reaching three and the average of this value being around four; 4) a small number (0.55%) of organs are multi-commit; 5) more than 40% of code reuse in the open-source software can be finished by organ transplantation. Based on our findings, we highlight implications for future studies and design the mode of using organs to conduct code reuse.

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Section: B Code Transplantationmentioning

confidence: 99%

Dissection on Java Organs in GitHub Repositories

Wang¹,

Mao²,

Yi³

2019

IEEE Access

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“…The genetic approach to code modification is one of the most popular approaches to automated software improvement [4] with recent progress in automated bug-fixing [5][6][7], optimising non-functional properties [8][9][10], and automatic test-case generation [6].…”

Section: Related Workmentioning

confidence: 99%

Experiments in genetic divergence for emergent systems

McGowan

Wild

Porter

2018

Proceedings of the 4th International Workshop on Genetic Improvement Workshop

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Emergent software systems take a step towards tackling the everincreasing complexity of modern software, by having systems selfassemble from a library of building blocks, and then continually re-assemble themselves from alternative building blocks to learn which compositions of behaviour work best in each deployment environment. One of the key challenges in emergent systems is populating the library of building blocks, and particularly a set of alternative implementations of particular building blocks, which form the runtime search space of optimal behaviour. We present initial work in using a fusion of genetic improvement and genetic synthesis to automatically populate a divergent set of implementations of the same functionality, allowing emergent systems to explore new behavioural alternatives without human input. Our early results indicate this approach is able to successfully yield useful divergent implementations of building blocks which are more suited than any existing alternative for particular operating conditions.

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“…The system can then automatically discover and replace the original shard with a more elaborately developed shard that includes error handling and corner case code. It is also possible to leave the core computation itself in place, then enhance the computation with automatically located and transferred checks from other programs [1].…”

Section: Error Handling and Corner Case Codementioning

confidence: 99%

“…There would be no replacement shard -the new shard or shards would enhance the recipient with new functionality not present before the insertion of the shard. CodePhage implements this shard transfer capability [1]. µScalpel transplants developer-identified procedures from a donor into a host [12].…”

Section: H Functionality Enhancement Via Shard Insertionmentioning

confidence: 99%

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Program fracture and recombination for efficient automatic code reuse

Amidon¹,

Davis

Sidiroglou-Douskos

et al. 2015

2015 IEEE High Performance Extreme Computing Conference (HPEC)

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Abstract-We present a new code transfer technique, program fracture and recombination, for automatically replacing, deleting, and/or combining code from multiple applications. Benefits include automatic generation of new applications incorporating the best or most desirable functionality developed anywhere, the automatic elimination of errors and security vulnerabilities, effective software rejuvenation, the automatic elimination of obsolete or undesirable functionality, and improved performance, energy efficiency, simplicity, analyzability, and clarity.The technique may be particularly appropriate for high performance computing. The field has devoted years of effort to developing efficient (but complex) implementations of standard linear algebra operations with good numerical properties. At the same time these operations also have very simple but inefficient implementations, often with poor numerical properties. Program fracture and recombination allows developers to work with the simple implementation during development and testing, then use program fracture and recombination to automatically find and deploy the most appropriate implementation for the hardware platform at hand. The benefits include reduced implementation effort, increased code clarity, and the ability to automatically search for and find efficient implementations with good numerical properties.

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Automatic error elimination by horizontal code transfer across multiple applications

Cited by 35 publications

References 45 publications

Dissection on Java Organs in GitHub Repositories

Dissection on Java Organs in GitHub Repositories

Experiments in genetic divergence for emergent systems

Program fracture and recombination for efficient automatic code reuse

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