8,291 research outputs found

    Neutral Networks of Real-World Programs and their Application to Automated Software Evolution

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    The existing software development ecosystem is the product of evolutionary forces, and consequently real-world software is amenable to improvement through automated evolutionary techniques. This dissertation presents empirical evidence that software is inherently robust to small randomized program transformations, or \u27mutations. Simple and general mutation operations are demonstrated that can be applied to software source code, compiled assembler code, or directly to binary executables. These mutations often generate variants of working programs that differ significantly from the original, yet remain fully functional. Applying successive mutations to the same software program uncovers large \u27neutral networks\u27 of fully functional variants of real-world software projects. These properties of \u27mutational robustness\u27 and the corresponding \u27neutral networks\u27 have been studied extensively in biology and are believed to be related to the capacity for unsupervised evolution and adaptation. As in biological systems, mutational robustness and neutral networks in software systems enable automated evolution. The dissertation presents several applications that leverage software neutral networks to automate common software development and maintenance tasks. Neutral networks are explored to generate diverse implementations of software for improving runtime security and for proactively repairing latent bugs. Next, a technique is introduced for automatically repairing bugs in the assembler and executables compiled from off-the-shelf software. As demonstration, a proprietary executable is manipulated to patch security vulnerabilities without access to source code or any aid from the software vendor. Finally, software neutral networks are leveraged to optimize complex nonfunctional runtime properties. This optimization technique is used to reduce the energy consumption of the popular PARSEC benchmark applications by 20% as compared to the best available public domain compiler optimizations. The applications presented herein apply evolutionary computation techniques to existing software using common software engineering tools. By enabling evolutionary techniques within the existing software development toolchain, this work is more likely to be of practical benefit to the developers and maintainers of real-world software systems

    Automatic Software Repair: a Bibliography

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    This article presents a survey on automatic software repair. Automatic software repair consists of automatically finding a solution to software bugs without human intervention. This article considers all kinds of repairs. First, it discusses behavioral repair where test suites, contracts, models, and crashing inputs are taken as oracle. Second, it discusses state repair, also known as runtime repair or runtime recovery, with techniques such as checkpoint and restart, reconfiguration, and invariant restoration. The uniqueness of this article is that it spans the research communities that contribute to this body of knowledge: software engineering, dependability, operating systems, programming languages, and security. It provides a novel and structured overview of the diversity of bug oracles and repair operators used in the literature

    Study of space shuttle environmental control and life support problems

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    Four problem areas were treated: (1) cargo module environmental control and life support systems; (2) space shuttle/space station interfaces; (3) thermal control considerations for payloads; and (4) feasibility of improving system reusability

    StubCoder: Automated Generation and Repair of Stub Code for Mock Objects

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    Mocking is an essential unit testing technique for isolating the class under test (CUT) from its dependencies. Developers often leverage mocking frameworks to develop stub code that specifies the behaviors of mock objects. However, developing and maintaining stub code is labor-intensive and error-prone. In this paper, we present StubCoder to automatically generate and repair stub code for regression testing. StubCoder implements a novel evolutionary algorithm that synthesizes test-passing stub code guided by the runtime behavior of test cases. We evaluated our proposed approach on 59 test cases from 13 open-source projects. Our evaluation results show that StubCoder can effectively generate stub code for incomplete test cases without stub code and repair obsolete test cases with broken stub code.Comment: This paper was accepted by the ACM Transactions on Software Engineering and Methodology (TOSEM) in July 202

    A Hybrid multi-agent architecture and heuristics generation for solving meeting scheduling problem

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    Agent-based computing has attracted much attention as a promising technique for application domains that are distributed, complex and heterogeneous. Current research on multi-agent systems (MAS) has become mature enough to be applied as a technology for solving problems in an increasingly wide range of complex applications. The main formal architectures used to describe the relationships between agents in MAS are centralised and distributed architectures. In computational complexity theory, researchers have classified the problems into the followings categories: (i) P problems, (ii) NP problems, (iii) NP-complete problems, and (iv) NP-hard problems. A method for computing the solution to NP-hard problems, using the algorithms and computational power available nowadays in reasonable time frame remains undiscovered. And unfortunately, many practical problems belong to this very class. On the other hand, it is essential that these problems are solved, and the only possibility of doing this is to use approximation techniques. Heuristic solution techniques are an alternative. A heuristic is a strategy that is powerful in general, but not absolutely guaranteed to provide the best (i.e. optimal) solutions or even find a solution. This demands adopting some optimisation techniques such as Evolutionary Algorithms (EA). This research has been undertaken to investigate the feasibility of running computationally intensive algorithms on multi-agent architectures while preserving the ability of small agents to run on small devices, including mobile devices. To achieve this, the present work proposes a new Hybrid Multi-Agent Architecture (HMAA) that generates new heuristics for solving NP-hard problems. This architecture is hybrid because it is "semi-distributed/semi-centralised" architecture where variables and constraints are distributed among small agents exactly as in distributed architectures, but when the small agents become stuck, a centralised control becomes active where the variables are transferred to a super agent, that has a central view of the whole system, and possesses much more computational power and intensive algorithms to generate new heuristics for the small agents, which find optimal solution for the specified problem. This research comes up with the followings: (1) Hybrid Multi-Agent Architecture (HMAA) that generates new heuristic for solving many NP-hard problems. (2) Two frameworks of HMAA have been implemented; search and optimisation frameworks. (3) New SMA meeting scheduling heuristic. (4) New SMA repair strategy for the scheduling process. (5) Small Agent (SMA) that is responsible for meeting scheduling has been developed. (6) “Local Search Programming” (LSP), a new concept for evolutionary approaches, has been introduced. (7) Two types of super-agent (LGP_SUA and LSP_SUA) have been implemented in the HMAA, and two SUAs (local and global optima) have been implemented for each type. (8) A prototype for HMAA has been implemented: this prototype employs the proposed meeting scheduling heuristic with the repair strategy on SMAs, and the four extensive algorithms on SUAs. The results reveal that this architecture is applicable to many different application domains because of its simplicity and efficiency. Its performance was better than many existing meeting scheduling architectures. HMAA can be modified and altered to other types of evolutionary approaches

    Evolutionary improvement of programs

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    Most applications of genetic programming (GP) involve the creation of an entirely new function, program or expression to solve a specific problem. In this paper, we propose a new approach that applies GP to improve existing software by optimizing its non-functional properties such as execution time, memory usage, or power consumption. In general, satisfying non-functional requirements is a difficult task and often achieved in part by optimizing compilers. However, modern compilers are in general not always able to produce semantically equivalent alternatives that optimize non-functional properties, even if such alternatives are known to exist: this is usually due to the limited local nature of such optimizations. In this paper, we discuss how best to combine and extend the existing evolutionary methods of GP, multiobjective optimization, and coevolution in order to improve existing software. Given as input the implementation of a function, we attempt to evolve a semantically equivalent version, in this case optimized to reduce execution time subject to a given probability distribution of inputs. We demonstrate that our framework is able to produce non-obvious optimizations that compilers are not yet able to generate on eight example functions. We employ a coevolved population of test cases to encourage the preservation of the function's semantics. We exploit the original program both through seeding of the population in order to focus the search, and as an oracle for testing purposes. As well as discussing the issues that arise when attempting to improve software, we employ rigorous experimental method to provide interesting and practical insights to suggest how to address these issues

    Automatic synthesis of sorting algorithms by gene expression programming + (geometric) semantic gene expression programming + encouraging phenotype variation with a new semantic operator: semantic conditional crossover

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    Gene Expression Programming (GEP) is an alternative to Genetic Programming (GP). Given its characteristics compared to GP, we question if GEP should be the standard choice for evolutionary program synthesis, both as base for research and practical application. We raise the question if such a shift could increase the rate of investigation, applicability and the quality of results obtained from evolutionary techniques for code optimization. We present three distinct and unprecedented studies using GEP in an attempt to develop understanding, investigate the potential and forward the branch. Each study has an individual contribution on its own involving GEP. As a whole, the three studies try to investigate di erent aspects that might be critical to answer the questions raised in the previous paragraph. In the rst individual contribution, we investigate GEP's applicability to automatically synthesize sorting algorithms. Performance is compared against GP under similar experimental conditions. GEP is shown to be capable of producing sorting algorithms and outperforms GP in doing so. As a second experiment, we enhanced GEP's evolutionary process with semantic awareness of candidate programs, originating Semantic Gene Expression Programming (SGEP), similarly to how Semantic Genetic Programming (SGP) builds over GP. Geometric semantic concepts are then introduced to SGEP, forming Geometric Semantic Gene Expression Programming (GSGEP). A comparative experiment between GP, GEP, SGP and SGEP is performed using di erent problems and setup combinations. Results were mixed when comparing SGEP and SGP, suggesting performance is signi cantly related to the problem addressed. By out-performing the alternatives in many of the benchmarks, SGEP demonstrates practical potential. The results are analyzed in di erent perspectives, also providing insight on the potential of di erent crossover variations when applied along GP/GEP. GEP' compatibility with innovation developed to work with GP is demonstrated possible without extensive adaptation. Considerations for integration of SGEP are discussed. In the last contribution, a new semantic operator is proposed, SCC, which applies crossover conditionally only when elements are semantically di erent enough, performing mutation otherwise. The strategy attempts to encourage semantic diversity and wider the portion of the semantic-solution space searched. A practical experiment was performed alternating the integration of SCC in the evolutionary process. When using the operator, the quality of obtained solutions alternated between slight improvements and declines. The results don't show a relevant indication of possible advantage from its employment and don't con rm what was expected in the theory. We discuss ways in which further work might investigate this concept and assess if it has practical potential under di erent circumstances. On the other hand, in regards to the basilar questions of this investigation, the process of development and testing of SCC is performed completely on a GEP/SGEP base, suggesting how the latest can be used as the base for future research on evolutionary program synthesis.Programa c~ao Gen etica por Express~oes (GEP) e uma alternativa recente a Programa c~ao Gen etica (GP). Neste estudo observamos o GEP e colocamos a quest~ao se este n~ao deveria ser tratado como primeira escolha quando se trata de sintetiza c~ao autom atica de programas atrav es de m etodos evolutivos. Dadas as caracteristicas do GEP perguntamonos se esta mudan ca de perspectiva poderia aumentar a investiga c~ao, aplicabilidade e qualidade dos resultados obtidos para a optimiza c~ao de c odigo por m etodos evolutivos. Neste estudo apresentamos tr^es contribui c~oes in editas e distintas usando o algoritmo GEP. Cada uma das contribui c~oes apresenta um avan co ou investiga c~ao no campo da GEP. Como um todo, estas contribui c~oes tentam obter cohecimento e informa c~oes para se abordar a quest~ao geral apresentada no p aragrafo anterior. Na primeira contribui c~ao, investiga-mos e testamos o GEP no problema da sintese autom atica de algoritmos de ordena c~ao. Para o melhor do nosso conhecimento, esta e a primeira vez que este problema e abordado com o GEP. A performance e comparada a do GP em condi c~oes semelhantes, de modo a isolar as caracteristicas de cada algoritmo como factor de distin c~ao. As a second experiment, we enhanced GEP's evolutionary process with semantic awareness of candidate programs, originating Semantic Gene Expression Programming (SGEP), similarly to how Semantic Genetic Programming (SGP) builds over GP. Geometric semantic concepts are then introduced to SGEP, forming Geometric Semantic Gene Expression Programming (GSGEP). A comparative experiment between GP, GEP, SGP and SGEP is performed using di erent problems and setup combinations. Results were mixed when comparing SGEP and SGP, suggesting performance is signi cantly related to the problem addressed. By out-performing the alternatives in many of the benchmarks, SGEP demonstrates practical potential. The results are analyzed in di erent perspectives, also providing insight on the potential of di erent crossover variations when applied along GP/GEP. GEP's compatibility with innovation developed to work with GP is demonstrated possible without extensive adaptation. Considerations for integration of SGEP are discussed. Na segunda contribui c~ao, adicionamos ao processo evolutivo do GEP a capacidade de medir o valor sem^antico dos programas que constituem a popula c~ao. A esta variante damos o nome de Programa c~ao Gen etica por Express~oes Sem^antica (SGEP). Esta variante tr as para o GEP as mesmas caracteristicas que a Programa c~ao Gen etica Sem^antica(SGP) trouxe para o GP convencional. Conceitos geom etricos s~ao tamb em apresentados para o SGEP, extendendo assim a variante e criando a Programa c~ao Gen etica por Express~oes Geom etrica Sem^antica (GSGEP). De forma a testar estas novas variantes, efectuamos uma experi^encia onde s~ao comparados o GP, GEP, SGP e SGEP entre diferentes problemas e combina c~oes de operadores de cruzamento. Os resultados mostraram que n~ao houve um algoritmo que se destaca-se em todas as experi^encias, sugerindo que a performance est a signi cativamente relacionada com o problema a ser abordado. De qualquer modo, o SGEP obteve vantagem em bastantes dos benchmarks, dando assim ind cios de pot^encial ter utilidade pr atica. De um modo geral, esta contribui c~ao demonstra que e possivel utilizar tecnologia desenvolvida a pensar em GP no GEP sem grande esfor co na adapta c~ao. No m da contribui c~ao, s~ao discutidas algumas considera c~oes sobre o SGEP. Na terceira contribui c~ao propomos um novo operador, o Cruzamento Sem^antico Condicional (SCC). Este operador, baseado na dist^ancia sem^antica entre dois elementos propostos, decide se os elementos s~ao propostos para cruzamento, ou se um deles e mutato e ambos re-introduzidos na popula c~ao. Esta estrat egia tem como objectivo aumentar a diversidade gen etica na popula c~ao em fases cruciais do processo evolutivo e alargar a por c~ao do espa co sem^antico pesquisado. Para avaliar o pot^encial deste operador, realizamos uma experi^encia pr atica e comparamos processos evolutivos semelhantes onde o uso ou n~ao uso do SCC e o factor de distin c~ao. Os resultados obtidos n~ao demonstraram vantagens no uso do SCC e n~ao con rmam o esperado em teoria. No entanto s~ao discutidas maneiras em que o conceito pode ser reaproveitado para novos testes em que possa ter pot^encial para demonstrar resultados possitivos. Em rela c~ao a quest~ao central da tese, visto este estudo ter sido desenvolvido com base em GEP/SGEP e visto a teoria do SCC ser compativel com GP, e demonstrado que um estudo geral a area da sintese de algoritmos por meios evolutivos, pode ser conduzido com base no GEP
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