Automated Transplantation of Call Graph and Layout Features into Kate

Marginean, Alexandru; Barr, Earl T.; Harman, Mark; Jia, Yunyi

doi:10.1007/978-3-319-22183-0_21

Cited by 23 publications

(18 citation statements)

References 16 publications

(20 reference statements)

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“…Then we extract three Java organs with real-world functions from three Java corpuses [1, 2, 3] and transplant them into a Java real-world application. To the best of our knowledge, this dataset, containing thirty-three real-world organs, is the largest one from open-source repository for transplantation study so far and it is different from datasets used by state-of-the-art tools like mu_scalpel [14]: it is extracted from codes added by developers in opensource environment while previous ones are from ready-made systems or software.…”

Section: B Datasetsmentioning

confidence: 99%

An Initial Step Towards Organ Transplantation Based on GitHub Repository

Wang

Mao

2018

IEEE Access

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Organ transplantation, which is the utilization of codes directly related to some specific functionalities to complete one's own program, provides more convenience for developers than traditional component reuse. However, recent techniques are challenged with the lack of organs for transplantation. Hence, we conduct an empirical study on extracting organs from GitHub repository to explore transplantation based on large-scale dataset. We analyze statistics from 12 representative GitHub projects and get the conclusion that 1) there are abundant practical organs existing in commits with "add" as a key word in the comments; 2) organs in this repository mainly possess four kinds of contents; 3) approximately 70% of the organs are easy-to-transplant. Implementing our transplantation strategy for different kinds of organs, we manually extract 30 organs in three different programming languages, namely Java, Python, and C, and make unit tests for them utilizing four testing tools (two for Java, one for Python, and one for C). At last, we transplant three Java organs into a specific platform for a performance check to verify whether they can work well in the new system. All the 30 organs extracted by our strategy possess good performances in unit test with the highest passing rate reaching 97% and the lowest one still passing 80% and the three Java organs work well in the new system, providing three new functionalities for the host. All the results indicate the feasibility of organ transplantation based on open-source repository, bringing new idea for code reuse.

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Section: B Datasetsmentioning

confidence: 99%

An Initial Step Towards Organ Transplantation Based on GitHub Repository

Wang

Mao

2018

IEEE Access

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“…GI is often applied to non-functional properties of software but perhaps it is most famous for improving program's functionality, e.g. by removing bugs [8,9,10,11,12,13,14,15,16] or adding to its abilities [17,18,19,20,21,22]. Non-functional improvements that have been considered or results reported include: faster code [23,24], code which uses less energy [25,26,27,28,29,30,31,32,33,34] or less memory [35], and automatic parallelisation [36,37,38] and automatic porting [39] and embedded systems [40,41,25,42,43,44,45] as well as refactorisation [46], reverse engineering [47,48] and software product lines [49,50].…”

Section: Genetic Improvementmentioning

confidence: 99%

Visualising the Search Landscape of the Triangle Program

Langdon¹,

Veerapen

Ochoa

2017

Lecture Notes in Computer Science

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Abstract. High order mutation analysis of a software engineering benchmark, including schema and local optima networks, suggests program improvements may not be as hard to find as is often assumed. 1) Bitwise genetic building blocks are not deceptive and can lead to all global optima. 2) There are many neutral networks, plateaux and local optima, nevertheless in most cases near the human written C source code there are hill climbing routes including neutral moves to solutions.

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“…Genetic Improvement has been used to improve the performance of existing software, e.g. by reducing runtime [Langdon and Harman, 2015b], energy [Bruce et al, 2015] or memory consumption [Wu et al, 2015], but (apart from software transplanting [Marginean et al, 2015] and automatic bug fixing [Le Goues et al, 2012]) it usually tries not to change programs' output. GGGP builds on Genetic Improvement which is very much hands off (i.e.…”

Section: Evolving Rnafoldmentioning

confidence: 99%

CUDA RNAfold

Langdon¹,

Lorenz²

2018

Preprint

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Spectra-Based Fault Localisation (SBFL) aims to assist de-bugging by applying risk evaluation formulae (sometimes called suspiciousness metrics) to program spectra and ranking statements according to the predicted risk. Designing a risk evaluation formula is often an intuitive process done by human software engineer. This paper presents a Genetic Programming approach for evolving risk assessment formulae. The empirical evaluation using 92 faults from four Unix utilities produces promising results 1. GP-evolved equations can consistently outperform many of the human-designed formulae, such as Tarantula, Ochiai, Jaccard, Ample, and Wong1/2, up to 5.9 times. More importantly, they can perform equally as well as Op2, which was recently proved to be optimal against If-Then-Else-2 (ITE2) structure, or even outperform it against other program structures. 1 The program spectra data used in the paper, as well as the complete empirical results, are available from:http://www.cs.ucl.ac.uk/staff/s.yoo/evolving-sbfl.html. UCL DEPARTMENT OF COMPUTER SCIENCE Research Note W. B. Langdon and Ronny Lorenz AbstractWe add CUDA GPU C program code to RNAfold to enable both it to be run on nVidia gaming graphics hardware and so that many thousands of RNA secondary structures can be computed in parallel. RNAfold predicts the folding pattern for RNA molecules by using O(n 3 ) dynamic programming matrices to minimise the free energy of treating them as a sequence of bases. We benchmark RNAfold on RNA STRAND and artificial sequences of upto 30 000 bases on two GPUs and a GPGPU Tesla. The speed up is variable but up to 14 times.

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Automated Transplantation of Call Graph and Layout Features into Kate

Cited by 23 publications

References 16 publications

An Initial Step Towards Organ Transplantation Based on GitHub Repository

An Initial Step Towards Organ Transplantation Based on GitHub Repository

Visualising the Search Landscape of the Triangle Program

CUDA RNAfold

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