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Journal articles on the topic 'Evolutionary software'

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Published: 4 June 2021
Last updated: 1 February 2022

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1

Harman, Mark. "Software Engineering Meets Evolutionary Computation." Computer 44, no. 10 (October 2011): 31–39. http://dx.doi.org/10.1109/mc.2011.263.

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2

Mantere, Timo, and Jarmo T. Alander. "Evolutionary software engineering, a review." Applied Soft Computing 5, no. 3 (March 2005): 315–31. http://dx.doi.org/10.1016/j.asoc.2004.08.004.

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3

Arcuri, Andrea. "Evolutionary repair of faulty software." Applied Soft Computing 11, no. 4 (June 2011): 3494–514. http://dx.doi.org/10.1016/j.asoc.2011.01.023.

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4

Bakar, Abubakar D., Abu Bakar Md Sultan, Hazura Zulzalil, and Jamilah Din. "Metric’s Thresholds for Encoding Evolutionary Computing Representation in Software Engineering Problem." Lecture Notes on Software Engineering 3, no. 1 (2015): 22–25. http://dx.doi.org/10.7763/lnse.2015.v3.158.

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5

Hu, X., and G. Greenwood. "Evolutionary approach to hardware/software partitioning." IEE Proceedings - Computers and Digital Techniques 145, no. 3 (1998): 203. http://dx.doi.org/10.1049/ip-cdt:19981972.

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6

Demidova, Anna. "Global Optimization Software and Evolutionary Algorithms." SHS Web of Conferences 29 (2016): 02009. http://dx.doi.org/10.1051/shsconf/20162902009.

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7

Kumar, S., K. Tamura, I. B. Jakobsen, and M. Nei. "MEGA2: molecular evolutionary genetics analysis software." Bioinformatics 17, no. 12 (December 1, 2001): 1244–45. http://dx.doi.org/10.1093/bioinformatics/17.12.1244.

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8

Salasin, John, and Howie Shrobe. "Evolutionary design of complex software (EDCS)." ACM SIGSOFT Software Engineering Notes 20, no. 5 (December 1995): 18–22. http://dx.doi.org/10.1145/217030.217033.

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9

Nguyen, Cu D., Simon Miles, Anna Perini, Paolo Tonella, Mark Harman, and Michael Luck. "Evolutionary testing of autonomous software agents." Autonomous Agents and Multi-Agent Systems 25, no. 2 (May 20, 2011): 260–83. http://dx.doi.org/10.1007/s10458-011-9175-4.

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10

Poo, Danny C. C. "Implementing an evolutionary structural software model." Journal of Systems and Software 22, no. 2 (August 1993): 81–90. http://dx.doi.org/10.1016/0164-1212(93)90087-e.

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11

Vignolo, Leandro D., Hugo L. Rufiner, Diego H. Milone, and John C. Goddard. "Evolutionary cepstral coefficients." Applied Soft Computing 11, no. 4 (June 2011): 3419–28. http://dx.doi.org/10.1016/j.asoc.2011.01.012.

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12

Sehra, Sumeet Kaur, Yadwinder Singh Brar, and Navdeep Kaur. "Evolutionary Computing Techniques for Software Effort Estimation." International Journal of Computer Science and Information Technology 9, no. 2 (April 30, 2017): 123–30. http://dx.doi.org/10.5121/ijcsit.2017.9211.

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13

Harman, Mark. "Evolutionary algorithms for software testing in Facebook." ACM SIGEVOlution 11, no. 2 (August 3, 2018): 7. http://dx.doi.org/10.1145/3264700.3264702.

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14

Krzanik, L. "Enactable models for quantitative evolutionary software processes." ACM SIGSOFT Software Engineering Notes 14, no. 4 (May 1989): 103–10. http://dx.doi.org/10.1145/75111.75127.

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15

Chen, Weiqi, Han Chen, Quansheng Guan, Fei Ji, and Bingyi Guo. "Evolutionary Sleep Scheduling in Software-Defined Networks." IEEE Access 6 (2018): 29541–50. http://dx.doi.org/10.1109/access.2018.2841985.

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16

Matchin, V. T. "APPLICATION OF EVOLUTIONARY MODELING FOR SOFTWARE REGENERATION." Educational resources and technology, no. 4 (2019): 42–52. http://dx.doi.org/10.21777/2500-2112-2019-4-42-52.

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17

Hofkin, Robert. "EVOLUTIONARY DEVELOPMENT OF SOFTWARE-INTENSIVE PRODUCT LINES." INCOSE International Symposium 4, no. 1 (August 1994): 548–55. http://dx.doi.org/10.1002/j.2334-5837.1994.tb01757.x.

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18

Darriba, Diego, Tomáš Flouri, and Alexandros Stamatakis. "The State of Software for Evolutionary Biology." Molecular Biology and Evolution 35, no. 5 (January 29, 2018): 1037–46. http://dx.doi.org/10.1093/molbev/msy014.

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19

Kanwal, Jaweria, Onaiza Maqbool, Hamid Abdul Basit, and Muddassar Azam Sindhu. "Evolutionary Perspective of Structural Clones in Software." IEEE Access 7 (2019): 58720–39. http://dx.doi.org/10.1109/access.2019.2913043.

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20

Lindvall, Mikael, Ioana Rus, Forrest Shull, Marvin Zelkowitz, Paolo Donzelli, Atif Memon, Victor Basili, et al. "An evolutionary testbed for software technology evaluation." Innovations in Systems and Software Engineering 1, no. 1 (March 11, 2005): 3–11. http://dx.doi.org/10.1007/s11334-005-0007-z.

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21

Arcuri, Andrea, and Xin Yao. "Co-evolutionary automatic programming for software development." Information Sciences 259 (February 2014): 412–32. http://dx.doi.org/10.1016/j.ins.2009.12.019.

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22

Poo, Danny C. C., and Paul J. Layzell. "An evolutionary structural model for software maintenance." Journal of Systems and Software 18, no. 2 (May 1992): 113–23. http://dx.doi.org/10.1016/0164-1212(92)90121-y.

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23

Nogueira, Ana Filipa, José Carlos Bregieiro Ribeiro, Francisco Fernández de Vega, and Mário Alberto Zenha-Rela. "Object-Oriented Evolutionary Testing." International Journal of Natural Computing Research 4, no. 4 (October 2014): 15–35. http://dx.doi.org/10.4018/ijncr.2014100102.

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In Object-Oriented Evolutionary Testing, metaheuristics are employed to select or generate Test Data for Object-Oriented software. The application of search-based strategies to the Software Testing of Object-Oriented Software is fairly recent and is yet to be investigated comprehensively; this article aims to explore, review and contextualize relevant literature and research in this area, while pinpointing open problems and setting grounds for future work.
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24

YING, Shi, Rui WANG, Lin LI, and Bo DONG. "Evolutionary deployment optimization for service-oriented software in cloud}{Evolutionary deployment optimization for service-oriented software in cloud." SCIENTIA SINICA Informationis 47, no. 6 (June 1, 2017): 715. http://dx.doi.org/10.1360/n112016-00200.

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25

WANG, Yong, Zi-Xing CAI, Yu-Ren ZHOU, and Chi-Xin XIAO. "Constrained Optimization Evolutionary Algorithms." Journal of Software 20, no. 1 (April 7, 2009): 11–29. http://dx.doi.org/10.3724/sp.j.1001.2009.00011.

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26

González-Torres, Antonio, Francisco J. García-Peñalvo, Roberto Therón-Sánchez, and Ricardo Colomo-Palacios. "Knowledge discovery in software teams by means of evolutionary visual software analytics." Science of Computer Programming 121 (June 2016): 55–74. http://dx.doi.org/10.1016/j.scico.2015.09.005.

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27

Rathee, Amit, and Jitender Kumar Chhabra. "Sensitivity Analysis of Evolutionary Algorithm for Software Reusability." MENDEL 25, no. 1 (June 24, 2019): 31–38. http://dx.doi.org/10.13164/mendel.2019.1.031.

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Fast and competitive software industry demands rapid development using Component Based Software Development (CBSD). CBSD is dependent on the availability of the high-quality reusable component libraries. Recently, evolutionary multi-objective optimization algorithms have been used to identify sets of reusable software components from the source-code of Object Oriented (OO) software, using different quality indicators (e.g. cohesion, coupling, etc.). Sometimes, these used quality indicators are quite sensitive towards the small variations in their values, although they should not be. Therefore, this paper analyzes the sensitivity of the evolutionary technique for three quality indicators used during the identification: Frequent Usage Pattern (FUP), Semantic and evolutionary coupling. The sensitivity analysis is performed on three widely used open-source OO software. The experimentation is performed by mutating the system to different degrees. Results of the empirical analysis indicate that the semantic parameter is most sensitive and important. Ignoring this feature highly degrades the quality; FUP relation is uniformly sensitive and evolutionary relations's sensitivity is non-uniform.
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28

Appleton, Luke, and Joshua Mackie. "Using Digital Organism Evolutionary Software in the Classroom." American Biology Teacher 81, no. 1 (January 1, 2019): 12–17. http://dx.doi.org/10.1525/abt.2019.81.1.12.

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We review software for exploring biological evolution from a fun and educational perspective. Our goal is to provide a background explanation of the methods used, terminology, and user experience, and learning outcomes of desktop and mobile evolutionary simulators. Freeware and commercial programs are detailed, with a discussion of how certain software packages can be used in introducing evolutionary theory.
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29

Zhang, Du. "A Value-Based Framework for Software Evolutionary Testing." International Journal of Software Science and Computational Intelligence 3, no. 2 (April 2011): 62–82. http://dx.doi.org/10.4018/jssci.2011040105.

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The fundamental objective in value-based software engineering is to integrate consistent stakeholder value propositions into the full extent of software engineering principles and practices so as to increase the value for software assets. In such a value-based setting, artifacts in software development such as requirement specifications, use cases, test cases, or defects, are not treated as equally important during the development process. Instead, they will be differentiated according to how much they are contributing, directly or indirectly, to the stakeholder value propositions. The higher the contributions, the more important the artifacts become. In turn, development activities involving more important artifacts should be given higher priorities and greater considerations in the development process. In this paper, a value-based framework is proposed for carrying out software evolutionary testing with a focus on test data generation through genetic algorithms. The proposed framework incorporates general principles in value-based software testing and makes it possible to prioritize testing decisions that are rooted in the stakeholder value propositions. It allows for a cost-effective way to fulfill most valuable testing objectives first and a graceful degradation when planned testing process has to be shortened.
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30

Parween, Rahat. "Overtime Planning Using Evolutionary Algorithms in Software Development." International Journal of Computer Sciences and Engineering 7, no. 4 (April 30, 2019): 536–39. http://dx.doi.org/10.26438/ijcse/v7i4.536539.

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31

Jamil, Muhammad Abid, Mohamed K. Nour, Ahmad Alhindi, Normi Sham Awang Abhubakar, Muhammad Arif, and Tareq Fahad Aljabri. "Towards Software Product Lines Optimization Using Evolutionary Algorithms." Procedia Computer Science 163 (2019): 527–37. http://dx.doi.org/10.1016/j.procs.2019.12.135.

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32

Aguilar-Ruiz, Jesús S., Isabel Ramos, José C. Riquelme, and Miguel Toro. "An evolutionary approach to estimating software development projects." Information and Software Technology 43, no. 14 (December 2001): 875–82. http://dx.doi.org/10.1016/s0950-5849(01)00193-8.

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33

Lungu, Mircea, Michele Lanza, and Oscar Nierstrasz. "Evolutionary and collaborative software architecture recovery with Softwarenaut." Science of Computer Programming 79 (January 2014): 204–23. http://dx.doi.org/10.1016/j.scico.2012.04.007.

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34

Wappler, Stefan, Joachim Wegener, and André Baresel. "Evolutionary testing of software with function-assigned flags." Journal of Systems and Software 82, no. 11 (November 2009): 1767–79. http://dx.doi.org/10.1016/j.jss.2009.06.037.

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35

Wang, Muchou, Weifeng Pan, Bo Jiang, and Chenxiang Yuan. "CLEAR: Class Level Software Refactoring Using Evolutionary Algorithms." Journal of Intelligent Systems 24, no. 1 (March 1, 2015): 85–97. http://dx.doi.org/10.1515/jisys-2013-0058.

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AbstractThe original design of a software system is rarely prepared for every new requirement. Software systems should be updated frequently, which is usually accompanied by the decline in software modularity and quality. Although many approaches have been proposed to improve the quality of software, a majority of them are guided by metrics defined on the local properties of software. In this article, we propose to use a global metric borrowed from the network science to detect the moving method refactoring. First, our approach uses a bipartite network to represent classes, features (i.e., methods and fields), and their dependencies. Second, a new metric is introduced to quantify the modularity of a software system as a whole. Finally, a crossover-only evolutionary algorithm that uses the metric as its fitness function is introduced to optimize the class structure of a software system and detect the methods that should be moved. Empirical results on the benchmark Java projects show that our approach can find meaningful methods that should be moved with a high stability. The advantages of our approach are illustrated in comparison with some other approaches, specifically one refactoring approach, namely search-based refactoring approach (SBRA), and two community detection algorithms, namely a graph theoretic clustering algorithm (MCODE) and a fast algorithm for community detection (FG). Our approach provides a new way to do refactoring from the perspective of software structure.
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36

Nicholas, S. "Review: Software Projects: Evolutionary vs Big-Bang Delivery." Computer Bulletin 39, no. 6 (December 1, 1997): 30–31. http://dx.doi.org/10.1093/combul/39.6.30-c.

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37

Alhammad, Noor Khalaf L., Esra Alzaghoul, Fawaz A. Alzaghoul, and Mohammed Akour. "Evolutionary neural network classifiers for software effort estimation." International Journal of Computer Aided Engineering and Technology 12, no. 4 (2020): 495. http://dx.doi.org/10.1504/ijcaet.2020.10027779.

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38

Alhammad, Noor Khalaf L., Esra Alzaghoul, Fawaz A. Alzaghoul, and Mohammed Akour. "Evolutionary neural network classifiers for software effort estimation." International Journal of Computer Aided Engineering and Technology 12, no. 4 (2020): 495. http://dx.doi.org/10.1504/ijcaet.2020.107445.

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39

Legendre, S., and J. Clobert. "ULM, a software for conservation and evolutionary biologists." Journal of Applied Statistics 22, no. 5-6 (November 1995): 817–34. http://dx.doi.org/10.1080/02664769524649.

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40

Sosnin, Petr. "Substantially Evolutionary Theorizing in Designing Software-Intensive Systems." Information 9, no. 4 (April 13, 2018): 91. http://dx.doi.org/10.3390/info9040091.

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41

Guleria, Disha, and Aman Kaushik. "A Novel Evolutionary Approach to Software Cost Estimation." International Journal of Computer Applications 126, no. 4 (September 17, 2015): 47–52. http://dx.doi.org/10.5120/ijca2015906045.

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42

Ramírez, Aurora, José Raúl Romero, and Sebastián Ventura. "Interactive multi-objective evolutionary optimization of software architectures." Information Sciences 463-464 (October 2018): 92–109. http://dx.doi.org/10.1016/j.ins.2018.06.034.

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43

Wang, Youbing, Hao Sun, and Lishan Kang. "The applications of evolutionary computation in software reliability." Wuhan University Journal of Natural Sciences 1, no. 3-4 (December 1996): 645–50. http://dx.doi.org/10.1007/bf02900900.

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44

Zitzler, E., J. Teich, and S. S. Bhattclcharyya. "Evolutionary algorithms for the synthesis of embedded software." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 8, no. 4 (August 2000): 452–55. http://dx.doi.org/10.1109/92.863627.

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45

Kumar, Sudhir, Koichiro Tamura, and Masatoshi Nei. "MEGA: Molecular Evolutionary Genetics Analysis software for microcomputers." Bioinformatics 10, no. 2 (1994): 189–91. http://dx.doi.org/10.1093/bioinformatics/10.2.189.

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46

Khosravi, Samiyeh, and . "New Software Testing Method Based on Evolutionary Algorithm." International Journal of Engineering & Technology 7, no. 3.27 (August 15, 2018): 142. http://dx.doi.org/10.14419/ijet.v7i3.27.17747.

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Software Testing is an important part of software development which is performed to support and enhance reliability and quality of software. It consists of estimating testing effort, selecting suitable test team, designing test cases, executing the software with those test cases and examining the results produced by these methods. This paper proposes Fuzzy Based approach for finding out the complexity weight based on requirement. Based on the weight, this paper computes the software complexity point. Fuzzy logic uses membership functions to include linguistic variables and quantifiers. Fuzzy Logic could be used in project estimation purposes efficiently by gathering size data on previously developed programs. The advantage of this model is that it is able to estimate the software complexity which in turn predicts the software requirement stability during the software development cycle.
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47

RAMAMOORTHY, C. V., DANIEL E. COOKE, and CHITTA BARAL. "MAINTAINING THE TRUTH OF SPECIFICATIONS IN EVOLUTIONARY SOFTWARE." International Journal on Artificial Intelligence Tools 02, no. 01 (March 1993): 15–31. http://dx.doi.org/10.1142/s0218213093000035.

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In this paper we explore how methods of truth maintenance and nonmonotonic reasoning may be applied in the engineering of integrated, dynamic systems. In the life of a large system, it is not possible to foresee every significant event which may affect the behavior of the system. Truth maintenance techniques may prove useful in the production and maintenance of these large systems. We discuss how it is that the decision making aspects of a system which change. Both the information on which we base our decisions and our strategies for decision making change. We discuss truth maintenance with respect to preserving the models of specifications and the models of programs. We discuss nonmonotonic reasoning and the need to have nonmonotonic program specification languages for evolutionary programs. We also discuss the relation between knowledge combination and software development.
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48

COOMBS, JASON A., B. H. LETCHER, and K. H. NISLOW. "pedagog : software for simulating eco‐evolutionary population dynamics." Molecular Ecology Resources 10, no. 3 (April 6, 2010): 558–63. http://dx.doi.org/10.1111/j.1755-0998.2009.02803.x.

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49

González-Torres, Antonio, Francisco J. García-Peñalvo, and Roberto Therón. "Human–computer interaction in evolutionary visual software analytics." Computers in Human Behavior 29, no. 2 (March 2013): 486–95. http://dx.doi.org/10.1016/j.chb.2012.01.013.

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50

Chen, Jianfeng, Vivek Nair, and Tim Menzies. "Beyond evolutionary algorithms for search-based software engineering." Information and Software Technology 95 (March 2018): 281–94. http://dx.doi.org/10.1016/j.infsof.2017.08.007.

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