Relevant bibliographies by topics / Evolutionary software

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Evolutionary software'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Evolutionary software"

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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Evolutionary software"

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Schreiber, Jonah, and Philip Bramstång. "Evolutionary Tuning of Chess Playing Software." Thesis, KTH, Fysik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-126387.

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In the ambition to create intelligent computer players, the game of chess is probably the most well-studied game. Much work has already been done on producing good methods to search a chess game tree and to statically evaluate chess positions. However, there is little consensus on how to tune the parameters of a chess program’s search and evaluation functions. What set of parameters makes the program play its strongest? This paper attempts to answer this question by observing the results of tuning a custom chess-playing implementation, called Agent, using genetic algorithms and evolutionary programming. We show not only how such algorithms improve the program’s playing strength overall, but we also compare the improved program’s strength to other versions of Agent.
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Bronson, Darren (Darren Frazier) 1970. "Best practices for evolutionary software development." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80490.

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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.
Includes bibliographical references (p. 41-42).
by Darren Bronson.
S.M.
M.B.A.
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Davis, Edward V. Jr. "Software testing for evolutionary iterative rapid prototyping." Thesis, Monterey, California: Naval Postgraduate School, 1990. http://hdl.handle.net/10945/27570.

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Approved for public release; distribution unlimited.
Rapid prototyping is emerging as a promising software development paradigm. It provides a systematic and automatable means of developing a software system under circumstances where initial requirements are not well known or where requirements change frequently during development. To provide high software quality assurance requires sufficient software testing. The unique nature of evolutionary iterative prototyping is not well-suited for classical testing methodologies, therefore the need exists for a testing methodology tailored for this prototyping paradigm. This thesis surveys current prototyping and testing practices to provide a foundation for developing a software testing methodology for prototyping. The thesis then describes a testing methodology for rapid prototyping, Spiral Testing, and the Test Goal Tracking System (TGTS), a requirements-based testing tool developed for use with the Computer Aided Prototyping System (CAPS). TGTS provides the first in an anticipated family of testing tools to support the CAPS environment. This thesis shows key prototyping characteristics impinging on testing, the value of Spiral Testing and the feasibility and qualities of complementary testing tools to support evolutionary iterative rapid prototyping.
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Gross, Hans-Gerhard. "Measuring evolutionary testability of real-time software." Thesis, University of South Wales, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365087.

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Davis, Edward V. "Software testing for evolutionary iterative rapid prototyping." Monterey, California : Naval Postgraduate School, 1990. http://handle.dtic.mil/100.2/ADA232555.

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Thesis (M.S. in Computer Science)--Naval Postgraduate School, December 1990.
Thesis Advisor(s): Shimeall, Timothy J. Second Reader: Barnes, Patrick D. "December 1990." Description based on title screen as viewed on March 29, 2010. DTIC Identifier(s): Computer Program Verification, Prototypes, Software Engineering, User Manuals. Author(s) subject terms: Software Testing, Software Prototyping, Rapid Prototyping, Reusable Components, Requirements-based Testing, Software Testing Tools. Includes bibliographical references (p. 276-281). Also available in print.
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Wilkerson, Joshua Lee. "Co-evolutionary automated software correction: a proof of concept." Diss., Rolla, Mo. : Missouri University of Science and Technology, 2008. http://scholarsmine.mst.edu/thesis/pdf/Wilkerson_09007dcc80642bb4.pdf.

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Thesis (M.S.)--Missouri University of Science and Technology, 2008.
Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed June 18, 2009) Includes bibliographical references (p. 62-64).
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Forsyth, Henry Lee. "Evolutionary environmental modelling in self-managing software systems." Thesis, Liverpool John Moores University, 2010. http://researchonline.ljmu.ac.uk/6009/.

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Over recent years, the increasing richness and sophistication of modem software systems has challenged conventional design-time software modelling analysis and has led to a number of studies exploring non-conventional approaches particularly those inspired by nature. The natural world routinely produces organisms that can not only survive but also flourish in changing environments as a consequence of their ability to adapt and therefore improve their fitness in relation to the external environments in which they exist. Following this biologically inspired systems' design approach, this study aims to test the hypothesis - can evolutionary techniques for runtime modelling of a given system's environment be more effective than traditional approaches, which are increasingly difficult to specify and model at design-time? This work specifically focuses on investigating the requirements for software environment modelling at runtime via a proposed systemic integration of Learning Classifier Systems and Genetic Algorithms with the well-known managerial cybernetics Viable Systems Model. The main novel contribution of this thesis is that it provides an evaluation of an approach by which software can create and crucially, maintain a current model of the environment, allowing the system to react more effectively to changes in that environment, thereby improving robustness and performance of the system. Detailed novel contributions include an evaluation of a variety of environmental modelling approaches to improving system robustness, the use of Learning Classifier Systems and genetic algorithms to provide the modelling element required of effective adaptive software systems. It also provides a conceptual framework of an Environmental Modelling, Monitoring and Adaptive system (EMMA) to manage the various elements required to achieve an effective environmental control system. The key result of this research has been to demonstrate the value of the guiding principles provided by the field of cybernetics and the potential of Beer's 2 cybernetically based Viable System Model in providing a learning framework, and subsequently a roadmap, to developing self-managing autonomic systems. The work is presented using a virtual world platform called "Second Life". This platform was used for experimental design and testing of results.
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Chatterjee, Raja. "KBMS-based evolutionary prototyping of object-oriented software systems /." CLICK ON LINK, THEN SEARCH BY AUTHOR OR TITLE; ACCESS RESTRICTED TO COMPUTERS ON UF CAMPUS OR WITH UF IP ADDRESSES, 1998. http://wwwlib.umi.com/cr/ufl/main.

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Thesis (Ph. D.)--University of Florida, 1998.
Typescript. Vita. Includes bibliographical references (leaves 115-118). Full text also available from UMI Current Research @ database; Adobe Acrobat Reader required to display text; see LINKS to connect
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Simons, Christopher Lloyd. "Interactive evolutionary computing in early lifecycle software engineering design." Thesis, University of the West of England, Bristol, 2011. http://eprints.uwe.ac.uk/15599/.

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Design is fundamental to software development. Indeed, early lifecycle software engineering design is crucial and has significant impact of subsequent development activities. Inferior designs can result in deleterious down-stream consequences. Therefore improving the traceability, structural integrity and elegance of software design has significant potential for enhancing software development productivity. However, early lifecycle software design is a demanding and non-trivial task for software engineers to perform and current computational tool support for software engineers is limited. Thus to address this limitation, this thesis investigates the potential of interactive evolutionary search and complementary computational intelligence to enable the exploration and discovery of useful and interesting software designs relating to the design problem at hand. To enable evolutionary search and exploration of possible design solutions, a novel, discrete, object-based representation of both design problem and design solution is proposed. Associated genetic operators including self-adapting mutation are also proposed. Experiments show that this novel representation enables highly effective search and exploration of the software design solution space. Next, software agents are introduced to facilitate an interactive framework for natural collaborative designer / computer interaction. Empirical investigations reveal that colourful visualisation of software designs engages the designer. Furthermore, with enhanced generation of multiple candidate designs, opportunities for periods of designer reflection are presented thus enabling sudden design discovery. Design elegance is an important but complex factor in software design. Four novel quantitative elegance measures are proposed which enhance the interactive design experience by selecting elegant software designs for designer evaluation. Using designer elegance evaluation as reward, reward-based machine learning is exploited to steer a dynamic, multi-objective search according to designer elegance intentions. Designer interactivity is further enhanced by a dynamic, fitness-proportionate interactive interval, which judiciously varies the number of evolutionary generations between interactions to promote search and exploration and further reduce use fatigue. The integration of interactive, dynamic evolutionary search with software agents and reward-based learning is found to produce an engaging, compelling interactive experience for software designers, successfully enabling the search, exploration and discovery of fruitful, interesting and useful early lifecycle software designs.
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Harkin, James. "Hardware software partitioning : a reconfigurable and evolutionary computing approach." Thesis, University of Ulster, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.274414.

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Books on the topic "Evolutionary software"

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Rapid evolutionary development: Requirements, prototyping & software creation. New York: Wiley, 1991.

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Connell, John L. Structured rapid prototyping: An evolutionary approach to software development. Englewood Cliffs, N.J: Yourdon Press, 1989.

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Harkin, James. Hardware-software partitioning: A reconfigurable and evolutionary computing approach. [S.l: The author], 2001.

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Ghanea-Hercock, Robert. Applied Evolutionary Algorithms in Java. New York, NY: Springer New York, 2003.

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J, Sadalage Pramod, ed. Refactoring databases: Evolutionary database design. Upper Saddle River, NJ, USA: Addison Wesley, 2006.

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Parmee, Ian C. Evolutionary and Adaptive Computing in Engineering Design. London: Springer London, 2001.

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Kallel, Leila. Theoretical Aspects of Evolutionary Computing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001.

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Larrañaga, Pedro. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Boston, MA: Springer US, 2002.

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1966-, Ambler Scott W., ed. Recipies for continuous database integration: Evolutionary database development. Boston, Mass: Addison Wesley Professional, 2007.

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Spears, William M. Evolutionary Algorithms: The Role of Mutation and Recombination. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000.

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Book chapters on the topic "Evolutionary software"

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Gillies, Alan C., and Peter Smith. "Evolutionary not revolutionary change." In Managing Software Engineering, 73–89. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4899-7188-3_6.

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Punter, Teade, Adam Trendowicz, and Peter Kaiser. "Evaluating Evolutionary Software Systems." In Product Focused Software Process Improvement, 258–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36209-6_23.

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Beckert, Bernhard, Jakob Mund, Mattias Ulbrich, and Alexander Weigl. "Formal Verification of Evolutionary Changes." In Managed Software Evolution, 309–32. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13499-0_11.

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Eberlein, Martin, Yannic Noller, Thomas Vogel, and Lars Grunske. "Evolutionary Grammar-Based Fuzzing." In Search-Based Software Engineering, 105–20. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59762-7_8.

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Smith, Jim, and T. C. Fogarty. "Evolving software test data — GA's learn self expression." In Evolutionary Computing, 137–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/bfb0032779.

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Simon, Daniel, and Thomas Eisenbarth. "Evolutionary Introduction of Software Product Lines." In Software Product Lines, 272–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45652-x_17.

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Sharma, Tarun Kumar, and Millie Pant. "Chapter 6 Swarm Intelligence In Software Engineering Design Problems." In Evolutionary Computation, 163–94. 3333 Mistwell Crescent, Oakville, ON L6L 0A2, Canada: Apple Academic Press, 2016. http://dx.doi.org/10.1201/9781315366388-7.

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John, Isabel, and Karina Villela. "Evolutionary Product Line Scoping." In Software Product Lines: Going Beyond, 515–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15579-6_57.

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Alba, Enrique, and J. Francisco Chicano. "Software Testing with Evolutionary Strategies." In Rapid Integration of Software Engineering Techniques, 50–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11751113_5.

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Byrski, Aleksander, and Marek Kisiel-Dorohinicki. "Agents and Components in Software Engineering." In Evolutionary Multi-Agent Systems, 109–21. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-51388-1_4.

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Conference papers on the topic "Evolutionary software"

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Forrest, Stephanie, and Claire LeGoues. "Evolutionary software repair." In the fourteenth international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2330784.2330943.

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Wernick, P., T. Hall, and C. L. Nehaniv. "Software Evolutionary Dynamics Modelled as the Activity of an Actor-Network." In Second International IEEE Workshop on Software Evolvability. IEEE, 2006. http://dx.doi.org/10.1109/software-evolvability.2006.12.

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Rodríguez-Fortiz, M. J., P. Paderewski-Rodríguez, L. Garcia-Cabrera, and J. Parets-Llorca. "Evolutionary modelling of software systems." In the 4th international workshop. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/602461.602471.

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"EVOLUTIONARY SOFTWARE LIFE CYCLE FOR SELF-ADAPTING SOFTWARE SYSTEMS." In 7th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2005. http://dx.doi.org/10.5220/0002550202110216.

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Rajlich, Vaclav. "Education impact of evolutionary software development." In 2013 IEEE 26th Conference on Software Engineering Education and Training - (CSEE&T). IEEE, 2013. http://dx.doi.org/10.1109/cseet.2013.6595272.

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Vanya, Adam, Lennart Hofland, Steven Klusener, Piërre van de Laar, and Hans van Vliet. "Assessing software archives with evolutionary clusters." In 2008 IEEE 16th International Conference on Program Comprehension. IEEE, 2008. http://dx.doi.org/10.1109/icpc.2008.34.

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Silva, Lucas Serpa, and Maarten van Someren. "Evolutionary testing of object-oriented software." In the 2010 ACM Symposium. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1774088.1774326.

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Thuy-Linh Nguyen. "An evolutionary design for software systems." In 2010 Second World Congress on Nature and Biologically Inspired Computing (NaBIC 2010). IEEE, 2010. http://dx.doi.org/10.1109/nabic.2010.5716356.

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Benkoczi, Robert, Daya Gaur, Shahadat Hossain, Muhammad Khan, and Ajay Raj Tedlapu. "Evolutionary hot-spots in software systems." In ICSE '20: 42nd International Conference on Software Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377812.3390909.

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Lopes, Rodolfo Ayala, Thiago Macedo Gomes, and Alan Robert Resende de Freitas. "A symbolic evolutionary algorithm software platform." In GECCO '19: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3319619.3326828.

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Reports on the topic "Evolutionary software"

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Royce, Walker. Pragmatic Quality Metrics for Evolutionary Software Development Models. Fort Belvoir, VA: Defense Technical Information Center, January 1991. http://dx.doi.org/10.21236/ada243022.

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Coulter, Neal, Ira Monarch, Suresh Konda, and Marvin Carr. An Evolutionary Perspective of Software Engineering Research Through Co-Word Analysis. Fort Belvoir, VA: Defense Technical Information Center, March 1996. http://dx.doi.org/10.21236/ada310305.

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