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KMoS-RE: knowledge management on a strategy to requirements engineering

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Abstract

This paper introduces the KMoS-RE Strategy, a novel requirements engineering strategy to Informal Structured Domains. Requirements engineering aims to elicit, analyze, evaluate, consolidate, and manage the requirements of a software system. The complexity of this process depends on the application domain. In Informal Structured Domains, not all concepts and their relations are formally defined, most of the problems do not have algorithms to obtain solutions, and the domain specialists use large amounts of tacit knowledge to solve problems. These characteristics generate ambiguous, inappropriate, and incomplete requirements. It could generate an inadequate software solution, or it could be the cause of increasing the project development time. Therefore, it is important to use an appropriate requirements engineering strategy to minimize these problems. The objective of this study is to present the Knowledge Management on a Strategy to Requirements Engineering (KMoS-RE Strategy): a novel requirements engineering strategy oriented to the transformation and transference of knowledge, and with the aim to minimize the percentage of ambiguous, incomplete, and inappropriate requirements. The functionality and utility of the strategy is explained through its application to a real case study. The case study shows that using the KMoS-RE Strategy helps to internalize the domain knowledge, to clarify the solution idea, to reduce the ignorance of symmetry, to structure the domain knowledge, and to detect and correct wrong beliefs about the domain. All of these are performed in the early stage of the project development.

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References

  1. McLeod L, McDonell S (2011) Factors that affect software systems development project: a survey of research. ACM Computing Surveys 43(4):article 24

  2. Han W, Huang S (2008) An empirical analysis of risk components and performance on software projects. J Syst Softw 80(1):42–50

    Article  Google Scholar 

  3. Sharma A, Kushwaha D (2011) Natural language based component extraction from requirement engineering document and its complexity analysis. SIGSOFT Softw Eng Notes 36(1):1–14

    Article  MathSciNet  Google Scholar 

  4. Castro J, Kolp M, Mylopoulos J (2002) Towards requirement driven information systems engineering: the Tropos project. J Inf Syst 27(6):365–389

    Article  MATH  Google Scholar 

  5. van Lamsweerde, A (2008) Requirements engineering: from craft to discipline. In: The 6th ACM-SIGSOFT international symposium on foundations of software engineering (SIGSOFT’08/FSE-16), ACM, New York, pp 238–246

  6. Jureta I, Borgida A, Ernst N, Mylopoulos J (2010) Techne: towards a new generation of requirements modeling languages with goals, preferences, and inconsistency handling. In: The 18th IEEE international requirements engineering conference (RE ‘10), IEEE Computer Society, Washington, pp 115–124

  7. Zickert F (2010) Evaluation of the goal-oriented requirement engineering method KAOS. In: The 16th Americas conference on information system (AMCIS 2010), AIseL, Lima, Paper 17

  8. Pilat L, Kaindl H (2011) a knowledge management perspective of requirements engineering. In: The 5th international conference on research challenges in information science (RCIS), pp 1–12

  9. Polanyi M (1967) The tacit dimension. Routledge and K. Paul, London

    Google Scholar 

  10. Gacitua R, Ma L, Nuseibeh B, Piwek P, de Roeck AN, Rouncefield M, Sawyer P, Willis A, Yang H (2009) Making tacit requirement explicit. In: The 2nd international workshop on managing requirements knowledge (MARK ‘09), IEEE Computer Society, Atlanta, pp 40–44

  11. Olmos K, Rodas J, Fernández LF (2010) Pertinencia de la formalización de dominios semi-formalmente definidos en el análisis inteligente de datos. CULCyT: Cultura Científica y Tecnológica, 40–41:71–93

  12. IEEE (1998) Recommended practice for software requirements specifications. IEEE Std. 830–1998

  13. Jackson M (1995) The world and the machine. In: The 17th international conference on software engineering, ACM, New York, pp 283–292

  14. Nonaka I, Takeuchi H (1995) The knowledge creation company. Oxford University Press, New York

    Google Scholar 

  15. Nonaka I (2009) Tacit knowledge conversion: controversy and advancement in organizational knowledge creation theory. J Org Sci 20(3):635–652

    Article  Google Scholar 

  16. Bjørner D (2010) The role of domain engineering in software development. Why current requirements engineering is flawed! Perspectives of Systems Informatics Lecture Notes in Computer Science, pp 1–34

  17. Lynch CF, Ashley K, Aleven V, Pinkwart N (2006) Defining Ill-Defind domain: a literature survey. In: Aleven V, Ashley K, Pikwart N (eds) Workshop on intelligent tutoring system for Ill-defined domains at the 8th international conference on intelligent tutoring system. National Central University, Jhongly, pp 1–10

    Google Scholar 

  18. Vessey I (2007) The effect of the application domain in is problem solving: a theorical analysis. In: Hart D (ed) Information system foundations, theory, representation and reality. Anu E Press, Australia

    Google Scholar 

  19. Gibert K, Annicchiarico R, Cortés U, Caltagirone C (2008) Advantages of combining AI and statistic for knowledge discovery on functional disability. Multivariate analysis of assessment scales using clustering based on rules. J Acta Informatica Medica 17(4):198–204

    Google Scholar 

  20. Goel V (1992) Comparison of well-structured and Ill-structured task environment and problem spaces. In: The 14th annual conference of the cognitive science, Lawrence Erlbaum Associates, Hillsdale, NJ, pp 844–849

  21. Jonassen D (1997) Instructional design models for Well-Structured and Ill-Structured problem-solving learning outcomes. J Educ Technol Res Dev 45(1):65–94

    Article  Google Scholar 

  22. Le NT, Menze W, Pinkwart N (2010) Considering Ill-definedness of problems from the aspect of solution. In: The 23rd international FLAIRS conference, special track on intelligent tutoring systems

  23. Simon H (1973) The structure of Ill-structured problems. J Artif Intell 4(3–4):181–201

    Article  Google Scholar 

  24. Mitrovic A, Weerasinghe A (2009) Revisiting Ill-definedness and the consequences for ITSs. In: Dimitrova V, Mizoguchi R, du Boulay B, Graesser A (eds) Knowledge representation to affective modelling at the proceedings of the 2009 conference on artificial intelligence in education: building learning systems that care. IOS Press, Amsterdam, The Netherlands, pp 375–382

  25. Fournier-Viger P, Nkambou R, Nquifo E (2010) Building intelligent tutoring systems for Ill-Defined domains. Adv Intell Tutoring Syst 308:81–101

    Google Scholar 

  26. Wyatt J (2001) Management of explicit and tacit knowledge. J R Soc Med 94:6–9

    Google Scholar 

  27. Fernandes K (2009) Interactive situation modeling in knowledge and intensive domains. J Bus Inf Syst 4(1):25–46

    Google Scholar 

  28. Fenstermacher K (2005) The tyranny of tacit knowledge: what artificial intelligence tell us about knowledge representation. In: The 38th annual Hawaii international conference on system sciences (HICSS ‘05), vol 8, IEEE Computer Society, Washington, pp 1–10

  29. Gourlay S (2002) Tacit knowledge, tacit knowing or behaving? In: The 3rd European conference on organizational knowledge, learning and capabilities (OKLC 2002), Athens, Greece, pp 2–23

  30. Ma L, Nuseibeh B, Piwek P, De Roeck A, Willis A (2009) On presuppositions in requirements. In: The 2nd international workshop on managing requirements knowledge (MARK ‘09), IEEE Computer Society, Atlanta pp 27–31

  31. Goguen J (1992) The dry and the wet. In: Proceedings of the IFIP TC8/WG8.1 working conference on information system concepts: improving the understanding, pp 1–17

  32. Goguen J (1996) Formality and informality in requirements engineering. In: Proceedings of the international conference on requirements engineering, pp 102–108

  33. Nuseibeh B, Easterbrook S (2000) Requirements engineering: a roadmap. In: Proceedings of the conference on the future of software engineering, ACM, (2000), pp 35–46

  34. Friedrich W, Van Der Poll J (2007) Towards a methodology to elicit tacit domain knowledge. J Inf Knowl Manag 2(1996):179–193

    Google Scholar 

  35. Li Y, Jun Mei C (2011) Tacit knowledge research in acquisition process of software requirements. In: International conference on business management and electronic information (BMEI) 2011, vol 2, IEEE, Guaggzhou, pp 641–645

  36. Stone A, Sawyer P (2006) Identifying tacit knowledge-based requirements. J IEEE Proc Softw 153(6):211–218

    Article  Google Scholar 

  37. Stoiber R, Glinz M (2009) Modeling and managing tacit product line requirements knowledge. In: The 2nd international managing requirements knowledge (MARK’09), IEEE Computer Society, Atlanta, pp 60–64

  38. Mohamed A (2010) Facilitating tacit-knowledge acquisition within requirements engineering. In: Fujita H, Sasaki J (eds) Proceedings of the 10th WSEAS international conference on applied computer science (ACS’10), world scientific and engineering academy and society (WSEAS), Stevens Point, Wisconsin, USA, pp 27–32

  39. Vázquez-Bravo D, Sánchez-Segura M, Medina-Dominguez F, Amescua A (2012) Combining software engineering elicitation technique with the knowledge management lifecycle. Int J Knowl Soc Res 3(1):1–13

    Article  Google Scholar 

  40. Wan J, Zhang H, Wan D, Huang D (2010) Research on knowledge creation in software requirement development. J Softw Eng Appl 3(5):487–494

    Article  Google Scholar 

  41. Sajid A, Nayyar A, Moshin A (2010) Modern trends towards requirement elicitation. In: The 2010 national software engineering conference (NSEC ‘10). ACM, New York

  42. Hossien A, Dieste O, García-Martínez R (2011) A process requirement conceptualization. Software Engineering. Methods and Teaching 2011:101–115

    Google Scholar 

  43. do Prado Leite JCS, Franco APM (1993) A strategy for conceptual model acquisition. In: The IEEE international symposium on requirements engineering. IEEE Press, San Diego, pp 243–246

  44. Sayão M, Carvalho G (2007) Lexicon construction for information systems. Revista Iberoamericana de Inteligencia Artificial 11(36):35–42

    Google Scholar 

  45. Hu H, Dan Y, Chunxiao Y, Chunlei F, Ren L (2011) Detecting interactions between behavioral requirements with OWL and SWRL. World Academy of Science, Engineering and Technology 72:330–336

    Google Scholar 

  46. Sagayaraj S, Ganapathy G (2009) Extraction of method signatures from ontology towards reusability for the given system requirement specification. In: Lectures notes in engineering and computer science, vol 21921, pp 1877–1882. News wood and International Association of Engineers, Hong-Kong

  47. Chuan D, Laurent P, Cleland-Huang J, Kwiatkowski C (2009) Towards automated requirements prioritization and triage. J Requir Eng 14(2):73–89

    Article  Google Scholar 

  48. Duan C, Cleland-Huang J, Mobasher B (2008) A consensus based approach to constrained clustering of software requirements. In: The 17th ACM conference on information and knowledge management (CIKM ‘08), ACM, New York, NY pp 1073–1082

  49. Olmos K, Rodas J, Fernández LF (2013) Requirements engineering knowledge model for informal structured domains. Int J Comp Commun Eng 2(1):75–77

    Article  Google Scholar 

  50. Alvarez R (2002) Discourse analysis of requirements and knowledge elicitation interviews. In: Proceedings of the 35th Hawaii international conference on system science (HICSS ‘35), IEEE

  51. van Lamsweerde A (2009) Requirements engineering: from system goals to UML models to software specifications. Wiley, Chichester

    Google Scholar 

  52. Bloom B, Engelhart M, Furst E, Hill W, Krathwohl D (1956) Taxonomy of educational objectives: the classification of educational goals. Handbook I: Cognitive domain, New York

  53. Mitri M (2003) Applying tacit knowledge management techniques for performance assessment. Comput Educ 41:173–189

    Article  Google Scholar 

  54. Nguyen L, Shanks G (2009) A framework for understanding creativity in requirement engineering. J Infor Soft Technol 51(3):655–662

    Article  Google Scholar 

  55. Cysneiros LM, do Prado Leite JCS (2004) Nonfunctional requirements: from elicitation to conceptual models. IEEE Trans Softw Eng 30(5):328–350

    Article  Google Scholar 

  56. Hadad G, Doorn J, Kaplan G (2009) Explicitar requisitos del software usando escenarios. In: Proceedings WER’09, workshop on requirements engineering

  57. Cockburn A (2001) Writing effective use cases, vol 1. Addison-Wesley

  58. Vanotti S (2008) Evaluación neuropsicológica en pacientes con esclerosis múltiple. Revista Argentina de Neuropsicología, vol 12, Buenos Aires, pp 13–21

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Olmos, K., Rodas, J. KMoS-RE: knowledge management on a strategy to requirements engineering. Requirements Eng 19, 421–440 (2014). https://doi.org/10.1007/s00766-013-0178-3

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