Relevant bibliographies by topics / Genetic improvement of software

Academic literature on the topic 'Genetic improvement of software'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Genetic improvement of software"

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Langdon, William B., Brian Yee Hong Lam, Marc Modat, Justyna Petke, and Mark Harman. "Genetic improvement of GPU software." Genetic Programming and Evolvable Machines 18, no. 1 (July 25, 2016): 5–44. http://dx.doi.org/10.1007/s10710-016-9273-9.

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Petke, Justyna, Saemundur O. Haraldsson, Mark Harman, William B. Langdon, David R. White, and John R. Woodward. "Genetic Improvement of Software: A Comprehensive Survey." IEEE Transactions on Evolutionary Computation 22, no. 3 (June 2018): 415–32. http://dx.doi.org/10.1109/tevc.2017.2693219.

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Brownlee, Alexander E. I. "Genetic Improvement @ ICSE 2021." ACM SIGSOFT Software Engineering Notes 46, no. 4 (October 27, 2021): 28–30. http://dx.doi.org/10.1145/3485952.3485960.

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Following Dr. Stephanie Forrest of Arizona State University's keynote presentation there was a wide ranging discussion at the tenth international Genetic Improvement workshop, GI-2021 @ ICSE (held as part of the International Conference on Software Engineering on Sunday 30th May 2021). Topics included a growing range of target systems and appli- cations, algorithmic improvements, wide-ranging questions about how other elds (especially evolutionary computation) can inform advances in GI, and about how GI is 'branded' to other disciplines. We give a personal perspective on the workshop's proceedings, the discussions that took place, and resulting prospective directions for future research.
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Langdon, W. B. "Big data driven genetic improvement for maintenance of legacy software systems." ACM SIGEVOlution 12, no. 3 (January 28, 2020): 6–9. http://dx.doi.org/10.1145/3381343.3381345.

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Jain, Rachna, and Arun Sharma. "ASSESSING SOFTWARE RELIABILITY USING GENETIC ALGORITHMS." Journal of Engineering Research [TJER] 16, no. 1 (May 9, 2019): 11. http://dx.doi.org/10.24200/tjer.vol16iss1pp11-17.

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The role of software reliability and quality improvement is becoming more important than any other issues related to software development. To date, we have various techniques that give a prediction of software reliability like neural networks, fuzzy logic, and other evolutionary algorithms. A genetic algorithm has been explored for predicting software reliability. One of the important aspects of software quality is called software reliability, thus, software engineering is of a great place in the software industry. To increase the software reliability, it is mandatory that we must design a model that predicts the fault and error in the software program at early stages, rectify them and then increase the functionality of the program within a minimum time and in a low cost. There exist numerous algorithms that predict software errors such as the Genetic Algorithm, which has a very high ability to predict software bugs, failure and errors rather than any other algorithm. The main purpose of this paper is to predict software errors with so precise, less time-consuming and cost-effective methodology. The outcome of this research paper is showing that the rates of applied methods and strategies are more than 96 percent in ideal conditions.
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López-López, Víctor R., Leonardo Trujillo, and Pierrick Legrand. "Applying genetic improvement to a genetic programming library in C++." Soft Computing 23, no. 22 (December 19, 2018): 11593–609. http://dx.doi.org/10.1007/s00500-018-03705-6.

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Petke, Justyna, Mark Harman, William B. Langdon, and Westley Weimer. "Specialising Software for Different Downstream Applications Using Genetic Improvement and Code Transplantation." IEEE Transactions on Software Engineering 44, no. 6 (June 1, 2018): 574–94. http://dx.doi.org/10.1109/tse.2017.2702606.

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Reza Mashinchi, M., and Ali Selamat. "An improvement on genetic-based learning method for fuzzy artificial neural networks." Applied Soft Computing 9, no. 4 (September 2009): 1208–16. http://dx.doi.org/10.1016/j.asoc.2009.03.011.

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Vijayalakshmi, K., N. Ramaraj, and R. Amuthakkannan. "Improvement of component selection process using Genetic Algorithm for Component-Based Software Development." International Journal of Information Systems and Change Management 3, no. 1 (2008): 63. http://dx.doi.org/10.1504/ijiscm.2008.019289.

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Hafiiak, A., E. Borodina, and A. Diachenko-Bohun. "APPLICATION OF GENETIC PROGRAMMING TOOLS AS A MEANS OF SOLVING OPTIMIZATION PROBLEMS." Системи управління, навігації та зв’язку. Збірник наукових праць 6, no. 52 (December 13, 2018): 58–60. http://dx.doi.org/10.26906/sunz.2018.6.058.

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Purpose. The article is devoted to the problem of practical application of genetic programming tools as a means of solving optimization problems and the use of genetic programming in various fields of activity. It is established that the evolution of genetic programming is directly related to the development of the genetic algorithm, it is also determined that with the passage of time a significant improvement in genetic programming has occurred. Since the advent of the genetic algorithm, many modifications and software implementations have appeared. This in turn led to the implementation of the genetic algorithm toolkit in software products, namely: specialized software, applications for mathematical and analytical packages, frameworks and libraries. The article reveals the significant impact of genetic programming in the areas of: quantum computing, electrical circuit design, etc. Not only advantages, but also disadvantages are considered, attention is also paid to methods of eliminating deficiencies by improving optimization methods and applying a genetic algorithm. Results. The analysis of the main directions of the practical use of genetic programming is carried out and tasks that can be effectively solved using this toolkit are outlined. Scientific novelty. It was determined that the improvement of optimization methods and the expansion of the use of genetic algorithms, stimulates the appearance of such software products on the market, simplifies the structure of software tools, designs the interface for working with a specific commercial user community, simplifies the command language, which allows the use of genetic programming tools circle of users with different levels of training.
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Dissertations / Theses on the topic "Genetic improvement of software"

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Wu, F. "Mutation-based genetic improvement of software." Thesis, University College London (University of London), 2017. http://discovery.ucl.ac.uk/1561361/.

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Genetic Improvement (GI) of software is a recent field that has drawn much attention from Software Engineering researchers. It aims to use search techniques to automatically modify and improve existing software. The drawback in previous GI approaches is scalability of these approaches, due to the large search space formed by the code base in real-world systems. To overcome the scalability challenge, more recent studies have confined the granularity of code modification at the statement level and applied a prior sensitivity analysis to further reduce the search space. However, some software improvements may require code changes at a finer level of granularity. This thesis demonstrates that, by combining with Mutation Testing techniques, GI can operate at this finer granularity while preserving scalability. The thesis applies Mutation Operators to automatically modify the source code of the target software. After a prior sensitivity analysis on First Order Mutants, "deep" (previously unavailable) parameters are exposed from the most sensitive locations, followed by a bi-objective optimisation process to fine tune them together with existing ("shallow") parameters. The objective is to improve both time and memory resources required by the computation. Since this approach relies on the selection of Mutation Operators and traditional Mutation Operators are not concerned with memory performance, the thesis proposes and evaluates Memory Mutation Operators in the Mutation Testing context. Using both traditional and Memory Mutation Operators, the thesis further seeks to improve the target software by searching for Higher Order Mutants (HOMs). The thesis presents the result of a code analysis study, which reveals that, among all the code modifications that contribute to the improvement, more than half of them require a finer control of the code, which our approach is better at than previous GI approaches.
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Husa, Jakub. "Genetické vylepšení software pro kartézské genetické programování." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-255458.

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Genetic programming is a nature-inspired method of programming that allows an automated creation and adaptation of programs. For nearly two decades, this method has been able to provide human-comparable results across many fields. This work gives an introduction to the problems of evolutionary algorithms, genetic programming and the way they can be used to improve already existing software. This work then proposes a program able to use these methods to improve an implementation of cartesian genetic programming (CGP). This program is then tested on a CGP implementation created specifically for this project, and its functionality is then verified on other already existing implementations of CGP.
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Haraldsson, Saemundur Oskar. "Genetic improvement of software : from program landscapes to the automatic improvement of a live system." Thesis, University of Stirling, 2017. http://hdl.handle.net/1893/26007.

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In today’s technology driven society, software is becoming increasingly important in more areas of our lives. The domain of software extends beyond the obvious domain of computers, tablets, and mobile phones. Smart devices and the internet-of-things have inspired the integra- tion of digital and computational technology into objects that some of us would never have guessed could be possible or even necessary. Fridges and freezers connected to social media sites, a toaster activated with a mobile phone, physical buttons for shopping, and verbally asking smart speakers to order a meal to be delivered. This is the world we live in and it is an exciting time for software engineers and computer scientists. The sheer volume of code that is currently in use has long since outgrown beyond the point of any hope for proper manual maintenance. The rate of which mobile application stores such as Google’s and Apple’s have expanded is astounding. The research presented here aims to shed a light on an emerging field of research, called Genetic Improvement ( GI ) of software. It is a methodology to change program code to improve existing software. This thesis details a framework for GI that is then applied to explore fitness landscape of bug fixing Python software, reduce execution time in a C ++ program, and integrated into a live system. We show that software is generally not fragile and although fitness landscapes for GI are flat they are not impossible to search in. This conclusion applies equally to bug fixing in small programs as well as execution time improvements. The framework’s application is shown to be transportable between programming languages with minimal effort. Additionally, it can be easily integrated into a system that runs a live web service.
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Bruce, Bobby R. "The blind software engineer : improving the non-functional properties of software by means of genetic improvement." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10052290/.

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Life, even in its most basic of forms, continues to amaze mankind with the complexity of its design. When analysing this complexity it is easy to see why the idea of a grand designer has been such a prevalent idea in human history. If it is assumed intelligence is required to undertake a complex engineering feat, such as developing a modern computer system, then it is logical to assume a creature, even as basic as an earthworm, is the product of an even greater intelligence. Yet, as Darwin observed, intelligence is not a requirement for the creation of complex systems. Evolution, a phenomenon without consciousness or intellect can, over time, create systems of grand complexity and order. From this observation a question arises - is it possible to develop techniques inspired by Darwinian evolution to solve engineering problems without engineers? The first to ask such a question was Alan Turing, a person considered by many to be the father of computer science. In 1948 Turing proposed three approaches he believed could solve complex problems without the need for human intervention. The first was a purely logicdriven search. This arose a decade later in the form of general problem-solving algorithms. Though successful in solving toy problems which could be sufficiently formalised, solving real-world problems was found to be infeasible. The second approach Turing called 'cultural search'. This approach would store libraries of information to then reference and provide solutions to particular problems in accordance to this information. This is similar to what we would now refer to as an expert system. Though the first expert system is hard to date due to differences in definition, the development is normally attributed to Feigenbaum, Bachanan, Lederberg, and Sutherland for their work, originating in the 1960s, on the DENRAL system. Turing's last proposal was an iterative, evolutionary technique which he later expanded on stating: "We cannot expect to find a good child-machine at the first attempt. One must experiment with teaching one machine and see how well it learns. One can then try another and see if it is better or worse. There is an obvious connection between this process and evolution". Though a primitive proposal in comparison to modern techniques, Turing clearly identified the foundation of what we now refer to as Evolutionary Computation (EC). EC borrows principles from biological evolution and adapts them for use in computer systems. Despite EC initially appearing to be an awkward melding between the two perpendicular disciplines of biology and computer science, useful ideas from evolutionary theory can be utilised in engineering processes. Just as man dreamt of flight from watching birds, EC researchers dream of self-improving systems from observing evolutionary processes. Despite these similarities, evolutionary inspired techniques in computer science have yet to build complex software systems from scratch. Though they have been successfully utilised to solve complex problems, such as classification and clustering, there is a general acceptance that, as in nature, these evolutionary processes take vast amounts of time to create complex structures from simple starting points. Even the best computer systems cannot compete with nature's ability to evaluate many millions of variants in parallel over the course of millennia. It is for this reason research into modifying and optimising already existing software, a process known as Genetic Improvement, has blossomed. Genetic Improvement (commonly referred to as 'GI') modifies existing software using search-based techniques with respect to some objective. These search-based techniques are typically evolutionary and, if not, are based on iterative improvement which we may view as a form of evolution. GI sets out to solve the 'last mile' problems of software development; problems that arise in software engineering close to completion, such as bugs or sub-optimal performance. It is the genetic improvement of non-functional properties, such as execution time and energy consumption, which we concern ourselves with in this thesis, as we find it to be the area of research which is the most interesting, and the most exciting. It is hoped that those referencing this thesis may share the same vision: that the genetic improvement of non-functional properties has the potential to transform software development, and that the work presented here is a step towards that goal. The thesis is divided into six chapters (inclusive of this 'Introduction' chapter). In Chapter 2 we explain the background material necessary to understand the content discussed later in the following chapters. From this, in Chapter 3, we highlight our investigations into the novel nonfunctional property of energy consumption which, in part, includes a study in how energy may be reduced via the approximation of output. We then expand on this in Chapter 4 by discussing our investigations into the applicability of GI in the domain of approximate computing, which covers a study into optimising the non-functional properties of software running on novel hardware - in this case, Android tablet devices. We then show, in Chapter 5, early research into how GI may be used to specialise software for specific hardware targets; in particular, how GI may automatically modify sequential code to run on GPUs. Finally, in Chapter 6 we discuss what relevant work is currently being undertaken by using the area of genetic improvement, and provide the reader with clear and concise take-away messages from this thesis.
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Oliveira, Vinícius Paulo Lopes de. "Uma proposta de representação e operadores genéticos para algoritmos evolucionários aplicados no reparo automatizado de software." Universidade Federal de Goiás, 2017. http://repositorio.bc.ufg.br/tede/handle/tede/7767.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
Maintenance and software repair are responsible for most of the cost of a software in the course of its life. Software repair through genetic evolution may repair errors and improve software, reducing its high cost. GenProg is a technique that uses this approach and through patches evolution it is capable to fix errors in large and small softwares. A patch composed by low-granularity operations compromise the manipulation of these operations. These operations consist of three subspaces: operation, location of application of the operation and what the operation will apply at the location of the fault (operator, fault and fix, respectively). The recombination and mutation operators applied to a low granulation representation limits the ability of the technique to navigate in search space efficiently. It is proposed the reformulation of the representation, in order to allow greater search capability. Theoretical analysis of the representation showed that the new representation has a greater locality than the original one. Through experimentation, validation and genotypic analysis it is shown that the proposed changes have led to a better performance with respect to the original operators and parameters in terms of efficiency, in the first experiments the operator UnifSingle with memorization was 48.88% more effective than the Original operator and then the operator OPSingle_V2 was 26% more effective than the operator UnifSingle with memorization. Some characteristics of these cross-operators were observed through a genotype distance analysis and their influence on the automatic software reapair problem. The proposed mutation operator shown superior results if compared to original. Combination between operator UniSingle with memorization showed the best efficacy among all combinations of operators and parameters (28.29% superior to the best result of the original GenProg).
Manutenção e reparo de software é responsável pela maior parte do custo de um software no decorrer de sua vida. O reparo de software por meio de evolução genética pode reparar erros e/ou melhorar softwares, diminuindo seu alto custo. GenProg é uma técnica em desenvolvimento que utiliza esta abordagem e por meio de evolução de patches é capaz de reparar erros em grandes e pequenos softwares. Um patch é composto por operações de edições de baixa granularidade o que compromete a separação e edição dessas operações. Essas operações são formadas por três subespaços: operação, local da aplicação da operação e o que a operação irá aplicar no local da falha (operator, fault, fix, respectivamente). Os operadores de recombinação e mutação aplicados às representações de baixa granularidade limita a habilidade da técnica de navegar no espaço de busca de forma eficiente. É proposto neste estudo, a reformulação da representação, do operador de cruzamento e mutação a fim de permitir uma maior capacidade de busca. Análises teóricas da representação demonstraram que a nova representação possui localidade maior que a original. Por meio de experimentações, validações e análises genotípicas é mostrado que as mudanças propostas levaram a uma melhoria em relação aos operadores e parâmetros originais em termos de eficácia, sendo que nos experimentos iniciais o operador UnifSingle com memorização apresentou eficácia 45,88% superior ao melhor caso do operador Original e em seguida o operador posteriormente proposto OPSingle_V2 apresentou eficácia 26% superior ao UnifSingle com memorização. Foram observadas algumas características desses operadores de cruzamento por meio de uma análise por distância genotípica e suas influências no problema de reparo automatizado de software. O operador de mutação proposto apresentou resultados superiores ao operador de mutação original e combinado com operador UnifSingle com memorização, apresentou a melhor eficácia entre todas as combinações de operadores e parâmetros.
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Elalmis, Mert Erkan. "Software Process Improvement." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12609042/index.pdf.

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In this thesis the software development process and in particular, the requirements management processes in a major software development company have been investigated. The current problems related to requirements quality and process performances have been identified. Process improvement measures have been proposed based on the suggestions found in the relevant literature. The current process and the improved version have been compared with respect to the process evaluation metrics proposed particularly for software process improvement.
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Pourkomeylian, Pouya. "Software practice improvement /." Göteborg : Göteborg university, 2002. http://catalogue.bnf.fr/ark:/12148/cb399559644.

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Sezer, Bulent. "Software Engineering Process Improvement." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608338/index.pdf.

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This thesis presents a software engineering process improvement study. The literature on software process improvement is reviewed. Then the current design verification process at one of the Software Engineering Departments of the X Company, Ankara, Tü
rkiye (SED) is analyzed. Static software development process metrics have been calculated for the SED based on a recently proposed approach. Some improvement suggestions have been made based on the metric values calculated according to the proposals of that study. Besides, the author'
s improvement suggestions have been discussed with the senior staff at the department and then final version of the improvements has been gathered. Then, a discussion has been made comparing these two approaches. Finally, a new software design verification process model has been proposed. Some of the suggestions have already been applied and preliminary results have been obtained.
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Nikitina, Natalja. "Software Process Improvement Framework." Doctoral thesis, KTH, Programvaruteknik och Datorsystem, SCS, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-141272.

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Many software development organizations today are keen on improving their software development processes in order to develop software products faster, cheaper or better. For that reason, Software Process Improvement (SPI) has received significant attention from the research community over the last few decades. Process maturity models have become widely known for benchmarking software processes against predefined practices and for identifying processes to be improved or implemented, whereas process improvement approaches were developed for guiding the actual process improvement process. However, despite a wide number of provided guidelines on how to standardize the processes and how to run process improvement efforts, only a few SPI initiatives have succeeded. About 70% of the SPI initiatives fail and a significant number do not even get started. Many studies argue that the success of the SPI initiatives is dependent on the organizational, social and managerial aspects of process improvement. Those aspects however are not sufficiently covered by the existing SPI approaches and models. The little knowledge on organizational, social and managerial aspects of SPI that is available is mostly scattered across the domain. Hence, there is lack of a holistic overview of the current SPI domain that provides sufficient coverage of organizational, social and managerial aspects of SPI. This thesis has explored the organizational, social and managerial aspects of SPI and placed them into the context of the SPI domain. Its main research result is Software Process Improvement Framework (SPIF). The framework provides an overview of the SPI domain and positions theories representing organizational, social and managerial aspects of SPI in the context of existing SPI approaches, models, methods and practices. SPIF is based on the existing theoretical framework for SPI environment proposed by Sami Zahran. The SPIF framework has been additionally complimented with four additional outcomes of this study. Those are: 1) a list of organizational, social and managerial factors facilitating SPI effort, 2) a list of contextual factors impacting process change, 3) a process model for guiding software method adoption, and 4) a checklist representing the properties of successful and sustainable SPI projects. The research was based on a strong industrial cooperation. As many as thirty software development organizations were involved in this research. Methodologically, the research was conducted in line with the inductive reasoning, which guided the research into building the knowledge from empirical studies. However, at some stages of this research, literature studies were incorporated. The main research methods of this study are action research and case studies, whereas data collection methods are primarily structured interviews, participatory observations and surveys. The thesis concludes that implementing a recommended software development processes or practices using well defined SPI approaches is not enough. In order to implement successful and lasting process improvement, organizations also need to consider organizational, social and managerial aspects of SPI. The SPIF framework and other results of this thesis may significantly benefit software development organizations that plan to conduct software process change, or have already done it. These organizations may use SPIF for getting an overview of the process improvement process and the theories, methods and tools that should support it. The other results of this thesis can be used for: 1) incorporating organizational, social and managerial aspects in process changes, 2) for adapting process improvements in various organizational contexts, 3) for guiding adoptions of new software development methods, and finally 4) for evaluating and improving process improvement efforts.

QC 20140213

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Konuralp, Zeynep. "Software Process Improvement In A Software Development Environment." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12609059/index.pdf.

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A software process improvement study is presented. The literature on software development processes and their improvement is reviewed. The current peer review process at Software Engineering Directorate of the X Company, Ankara, Tü
rkiye (XCOM) is studied and the static software development metrics based on a recent proposal have been evaluated. The static software metrics based improvement suggestions and the author&rsquo
s improvement suggestions discussed with the senior staff are compared. An improved peer review process is proposed. The static software development metrics have been evaluated on the improved process to see the impacts of the improvements. The improved process has been already implemented at XCOM and preliminary results have been obtained.
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Books on the topic "Genetic improvement of software"

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Genetic improvement of tomato. [S.l.]: Springer, 2012.

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Kalloo, G., ed. Genetic Improvement of Tomato. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-84275-7.

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1938-, Hunter Robin, and Thayer Richard H, eds. Software process improvement. Los Alamitos CA: IEEE Computer Society, 2001.

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Richardson, Ita, Per Runeson, and Richard Messnarz, eds. Software Process Improvement. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11908562.

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Richardson, Ita, Pekka Abrahamsson, and Richard Messnarz, eds. Software Process Improvement. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11586012.

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O’Connor, Rory V., Nathan Baddoo, Juan Cuadrago Gallego, Ricardo Rejas Muslera, Kari Smolander, and Richard Messnarz, eds. Software Process Improvement. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04133-4.

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Dingsøyr, Torgeir, ed. Software Process Improvement. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/b102170.

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O’Connor, Rory V., Nathan Baddoo, Kari Smolander, and Richard Messnarz, eds. Software Process Improvement. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85936-9.

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Conradi, Reidar, Tore Dybå, Dag Ingar Kondrup Sjøberg, and Tor Ulsund, eds. Software Process Improvement. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-32179-8.

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Abrahamsson, Pekka, Nathan Baddoo, Tiziana Margaria, and Richard Messnarz, eds. Software Process Improvement. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75381-0.

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Book chapters on the topic "Genetic improvement of software"

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Langdon, William B., David R. White, Mark Harman, Yue Jia, and Justyna Petke. "API-Constrained Genetic Improvement." In Search Based Software Engineering, 224–30. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47106-8_16.

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Petke, Justyna, William B. Langdon, and Mark Harman. "Applying Genetic Improvement to MiniSAT." In Search Based Software Engineering, 257–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39742-4_21.

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Langdon, William B. "Genetic Improvement of Software for Multiple Objectives." In Search-Based Software Engineering, 12–28. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22183-0_2.

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Didar-Al-Alam, S. M., S. M. Shahnewaz, Dietmar Pfahl, and Guenther Ruhe. "Analysis and Improvement of Release Readiness – A Genetic Optimization Approach." In Product-Focused Software Process Improvement, 164–77. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13835-0_12.

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Callan, James, and Justyna Petke. "Multi-objective Genetic Improvement: A Case Study with EvoSuite." In Search-Based Software Engineering, 111–17. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21251-2_8.

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Di Martino, Sergio, Filomena Ferrucci, Carmine Gravino, and Federica Sarro. "A Genetic Algorithm to Configure Support Vector Machines for Predicting Fault-Prone Components." In Product-Focused Software Process Improvement, 247–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21843-9_20.

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Burles, Nathan, Edward Bowles, Alexander E. I. Brownlee, Zoltan A. Kocsis, Jerry Swan, and Nadarajen Veerapen. "Object-Oriented Genetic Improvement for Improved Energy Consumption in Google Guava." In Search-Based Software Engineering, 255–61. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22183-0_20.

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Lim, Mingyi, Giovani Guizzo, and Justyna Petke. "Impact of Test Suite Coverage on Overfitting in Genetic Improvement of Software." In Search-Based Software Engineering, 188–203. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59762-7_14.

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Sharma, Shashank, and Sumit Srivastava. "GAE: A Genetic-Based Approach for Software Workflow Improvement by Unhiding Hidden Transactions of a Legacy Application." In Advances in Intelligent Systems and Computing, 127–39. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0341-8_12.

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Singh, Shree P. "Integrated Genetic Improvement." In Developments in Plant Breeding, 133–65. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-015-9211-6_6.

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Conference papers on the topic "Genetic improvement of software"

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Bokhari, Mahmoud A., Markus Wagner, and Brad Alexander. "Genetic improvement of software efficiency." In GECCO '20: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377929.3398109.

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Lopez-Herrejon, Roberto E., Lukas Linsbauer, Wesley K. G. Assunção, Stefan Fischer, Silvia R. Vergilio, and Alexander Egyed. "Genetic Improvement for Software Product Lines." In GECCO '15: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2739482.2768422.

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Langdon, William B., and Karina Zile. "Genetic improvement of computational biology software." In GECCO '17: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3067695.3082540.

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Blot, Aymeric, and Justyna Petke. "Stack-Based Genetic Improvement." In ICSE '20: 42nd International Conference on Software Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3387940.3392174.

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Rainford, Penny Faulkner, and Barry Porter. "Open Challenges in Genetic Improvement for Emergent Software Systems." In 2021 IEEE/ACM International Workshop on Genetic Improvement (GI). IEEE, 2021. http://dx.doi.org/10.1109/gi52543.2021.00018.

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Orlov, Michael. "Towards modular large-scale darwinian software improvement." In GECCO '18: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3205651.3208311.

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Landsborough, Jason, Stephen Harding, and Bryan Beabout. "Evolutionary fuzzing for genetic improvement." In ICSE '18: 40th International Conference on Software Engineering. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3194810.3194819.

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Krauss, Oliver, Hanspeter Mössenböck, and Michael Affenzeller. "Towards Knowledge-guided Genetic Improvement." In ICSE '20: 42nd International Conference on Software Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3387940.3392172.

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Blot, Aymeric, and Justyna Petke. "Synthetic Benchmarks for Genetic Improvement." In ICSE '20: 42nd International Conference on Software Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3387940.3392175.

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O'Brien, George, and John A. Clark. "Using Genetic Improvement to Retarget quantum Software on Differing Hardware." In 2021 IEEE/ACM International Workshop on Genetic Improvement (GI). IEEE, 2021. http://dx.doi.org/10.1109/gi52543.2021.00015.

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Reports on the topic "Genetic improvement of software"

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Ferguson, Pat, Gloria Leman, Prasad Perini, Susan Renner, and Girish Seshagiri. Software Process Improvement Works. Fort Belvoir, VA: Defense Technical Information Center, November 1999. http://dx.doi.org/10.21236/ada371804.

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Humphrey, Watts S. Introduction to Software Process Improvement. Fort Belvoir, VA: Defense Technical Information Center, June 1993. http://dx.doi.org/10.21236/ada305164.

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Gootwine, Elisha, Warren C. Foote, Moshe Shani, and H. Goot. Genetic Improvement of Sheep by Introduction of Foreign Genetic Information into Prolific Breeds. United States Department of Agriculture, August 1985. http://dx.doi.org/10.32747/1985.7566578.bard.

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Liblit, Ben. Continuous Improvement of Deployed Software Systems. Fort Belvoir, VA: Defense Technical Information Center, February 2011. http://dx.doi.org/10.21236/ada563562.

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Werth, Laurie H. Lecture Notes on Software Process Improvement. Fort Belvoir, VA: Defense Technical Information Center, February 1993. http://dx.doi.org/10.21236/ada265200.

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Fisher, Matt, Ron Damer, L. Scott Reed, and Richard Barbour. Software Acquisition Improvement Framework (SAIF) Definition. Fort Belvoir, VA: Defense Technical Information Center, August 1998. http://dx.doi.org/10.21236/ada351640.

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McFeeley, Robert S., David W. McKeehan, and Timothy Temple. Software Process Improvement Roadmap. User's Guide. Fort Belvoir, VA: Defense Technical Information Center, May 1995. http://dx.doi.org/10.21236/ada618234.

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Kausch, Albert, and Richard Rhodes. Research and Technology Development for Genetic Improvement of Switchgrass. Office of Scientific and Technical Information (OSTI), May 2017. http://dx.doi.org/10.2172/1357908.

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Paulish, Daniel J. Case Studies of Software Process Improvement Methods. Fort Belvoir, VA: Defense Technical Information Center, December 1993. http://dx.doi.org/10.21236/ada277289.

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Reisch, Bruce, Pinhas Spiegel-Roy, and Aliza Vardi. Tissue Culture and Gene Transfer for Genetic Improvement of Grapes. United States Department of Agriculture, November 1991. http://dx.doi.org/10.32747/1991.7599656.bard.

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