Skip to main content

Towards the Development of a Framework for Encouraging the Learning of SPICE Model by Using Knowledge Graphs

  • Conference paper
  • First Online:
Software Process Improvement and Capability Determination (SPICE 2015)

Abstract

Software process learning is a relevant aspect on software process improvement. In this paper, we present a framework based on knowledge graphs, in order to evaluate the expertise on ISO 15504 software model (SPICE). Having identified some papers related to the target of the research, we have proposed a framework with modules related to mechanisms, to extract both: information from IT workers and SPI models, for generating the corresponding knowledge graphs and matching them, to determine strengths and weaknesses in the learning process of the SPICE model.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Fuggetta, A.: Software Process: A Roadmap. In: 22nd International Conference on Software, Engineering (ICSE 2000), Future of Software. Engineering Track, Limerick, Irlanda. ACM (2000)

    Google Scholar 

  2. Florac, W., Park, R. Carleton, A.: Practical Software Measurement: Measuring for Process Management and Improvement, SEI Software Engineering Institute, Carnegie Mellon University, CMU/SEI-97-HB-003, Pittsburgh (1997)

    Google Scholar 

  3. IEEE Computer Society: SWEBOK, Guide to the Software Engineering Body of Knowledge (2004)

    Google Scholar 

  4. Huang, S.T., et al.: ADDIE Instruction Design and Cognitive Apprenticeship for Project-based Software Engineering Education in MIS, pp. 652–662. APSEC, IEEE Computer Society (2005)

    Google Scholar 

  5. Burnstein, I.: Practical software testing: A process-oriented approach, pp. 503–536. Springer Professional Computing, USA (2003)

    Google Scholar 

  6. Lyytinen, K., Robey, D.: Learning Failure in Information Systems Development. Information Systems Journal 9, 85–101 (1999)

    Article  Google Scholar 

  7. ISO/IEC 15504-1:2004, Information technology — Process assessment — Part 1: Concepts and vocabulary, ISO/IEC JTC1 (2004)

    Google Scholar 

  8. Jayara, N. et al.: Towards a Query-by-Example System for Knowledge Graphs. In: GRADES 2014 Proceedings of Workshop on Graph Data Management Experiences and Systems, pp. 1–6 (2014)

    Google Scholar 

  9. Zheng, W. et al.: Efficient Subgraph Skyline Search Over Large Graphs. In: CIKM 2014, Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 1529–1538 (2014)

    Google Scholar 

  10. Auer, S. et al.: DBpedia: A nucleus for a Web of open data, In: ISWC (2007)

    Google Scholar 

  11. Suchanek, F.M., et al.: YAGO: a core of semantic knowledge unifying WordNet and Wikipedia. In: WWW (2007)

    Google Scholar 

  12. Wu, W., et al.: Probase: a probabilistic taxonomy for text understanding. In: SIGMOD, pp. 481–492 (2012)

    Google Scholar 

  13. Bollacker, K. et al.: Freebase: a collaboratively created graph database for structuring human knowledge. In: SIGMOD, pp. 1247–1250 (2008)

    Google Scholar 

  14. Kitchenham, B.: A. Guidelines for performing Systematic Literature Reviews in Software Engineering (2007)

    Google Scholar 

  15. Huang, S.T., et al.: ADDIE Instruction Design and Cognitive Apprenticeship for Project-based Software Education in MIS. In: Proceedings of APSEC2005, pp. 652–662. IEEE CS Press (2005)

    Google Scholar 

  16. Oh Navarro, E.: Design and Evaluation of an Educational Software Process Simulation Environment and Associated Model. In: 18th Conference on Software Engineering Education & Training, pp. 25–32 (2005)

    Google Scholar 

  17. Kuhrmann, M. et al.: Teaching software process modeling. In: ICSE 2013 Proceedings of the 2013 International Conference on Software Engineering, pp. 1138–1147 (2013)

    Google Scholar 

  18. Srinivasan, J., Lundqvist, K.: A Constructivist Approach to Teaching Software Processes. In: 29th International Conference on Software Engineering (2007)

    Google Scholar 

  19. Hogan, J.M. et al.: Tight spirals and industry clients: the modern SE education experience. In: ACE 2005 Proceedings of the 7th Australasian conference on Computing education, vol. 42, pp. 217–222 (2005)

    Google Scholar 

  20. Messnarz, R., et al.: SPICE Level 3 - Experience with Using E-Learning to Coach the Use of Standard System Design Best Practices in Projects. Systems, Software and Services Process Improvement, Communications in Computer and Information Science 99, 213–221 (2010)

    Article  Google Scholar 

  21. Monsalve, E.S. et al.: Teaching software engineering with SimulES-W. In: 24th IEEE-CS Conference on Software Engineering Education and Training (CSEE&T), pp. 31–40 (2011)

    Google Scholar 

  22. Singer, L. et al.: Influencing the adoption of software engineering methods using social software. In: 34th International Conference on Software Engineering (ICSE), pp. 1325–1328. IEEE (2012)

    Google Scholar 

  23. Umarji, M., Seaman, C.: Predicting acceptance of Software Process Improvement. In: Proceeding HSSE 2005 Proceedings of the 2005 Workshop on Human and Social Factors of Software Engineering, vol. 30(4), pp. 1–6 (2005)

    Google Scholar 

  24. Robillard, P.N.: The learning component in social software engineering. In: SSE 2011 Proceedings of the 4th International Workshop on Social Software Engineering, pp. 19–22 (2011)

    Google Scholar 

  25. Cruz, R., et al.: Supporting the Software Process Improvement in Very Small Entities through E-learning: The HEPALE! Project, pp. 221–231. IEEE Computer Society, ENC (2009)

    Google Scholar 

  26. Rong, G. et al.: Where does experience matter in software process education? An experience report. In: IEEE 27th Conference on Software Engineering Education and Training (CSEE&T), pp. 129–138 (2014)

    Google Scholar 

  27. Amescua, A., et al.: Knowledge repository to improve agile development processes learning. IET Software 4(6), 434–444 (2010)

    Article  Google Scholar 

  28. Tian, K. et al.: Improving Software Engineering Education through Enhanced Practical Experiences. In: IEEE/ACIS 10th International Conference on Computer and Information Science (ICIS), pp. 292–297 (2011)

    Google Scholar 

  29. Moreno, A.M., et al.: Process Improvement from an Academic Perspective: How Could Software Engineering Education Contribute to CMMI Practices? IEEE Software 31(4), 91–97 (2014)

    Article  Google Scholar 

  30. Desarkar, M.S., et al.: Med-Tree: A user knowledge graph framework for medical applications. In: IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE), pp. 1–4 (2013)

    Google Scholar 

  31. Corby, O., Zucker, F.C.: The KGRAM Abstract Machine for Knowledge Graph Querying. In: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 1, pp. 338–341 (2010)

    Google Scholar 

  32. Hulliyah, K. & Kusuma, H.T.: Application of knowledge graph for making Text Summarization (Analizing a text of educational issues). In: International Conference on Information and Communication Technology for the Muslim World (ICT4M), pp. E79–E83 (2010)

    Google Scholar 

  33. Jayara, N. et al.: Towards a Query-by-Example System for Knowledge Graphs. In: GRADES 2014, Proceedings of Workshop on GRAph Data management Experiences and Systems, pp. 1–6 (2014)

    Google Scholar 

  34. Yang, S. et al.: SLQ: a user-friendly graph querying system. In: SIGMOD 2014 Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 893–896 (2014)

    Google Scholar 

  35. Genest, D., Chein, M.: A content-search information retrieval process based on conceptual graphs. Knowledge and Information Systems 8(3), 292–309 (2005)

    Article  Google Scholar 

  36. Wu, Y., et al.: Summarizing answer graphs induced by keyword queries. Proceedings of the VLDB Endowment VLDB Endowment Hompage archive 6(14), 1774–1785 (2013)

    Article  Google Scholar 

  37. Kaufmann, M., Wilke, G., Portmann, E., Hinkelmann, K.: Combining bottom-up and top-down generation of interactive knowledge maps for enterprise search. In: Buchmann, R., Kifor, C.V., Yu, J. (eds.) KSEM 2014. LNCS, vol. 8793, pp. 186–197. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  38. Bairi, R. B. et al.: Personalized classifiers: evolving a classifier from a large reference knowledge graph. In: IDEAS 2014, Proceedings of the 18th International Database Engineering & Applications Symposium, pp. 132–141 (2014)

    Google Scholar 

  39. Xin, W. et al.: A novel approach to concepts via knowledge graph theory and AFS theory. In: International Conference on Intelligent Control and Information Processing (ICICIP), pp. 87–92 (2010)

    Google Scholar 

  40. Hakkani-Tur, D. et al.: Using a knowledge graph and query click logs for unsupervised learning of relation detection. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8327–8331 (2013)

    Google Scholar 

  41. Lopes, S. et al.: Making Concept Maps Available on the Web to the Students. In: Computers and Education, pp 179–188 (2008)

    Google Scholar 

  42. Gurupur, V.P., et al.: Semantic requirements sharing approach to develop software systems using concept maps and information entropy: A Personal Health Information System example. Advances in Engineering Software 70, 25–35 (2014)

    Article  Google Scholar 

  43. Zimmermann, O., et al.: Managing architectural decision models with dependency relations, integrity constraints, and production rules. Journal of Systems and Software 82(8), 1249–1267 (2009)

    Article  Google Scholar 

  44. Ernst, P., et al.: KnowLife: A knowledge graph for health and life sciences. In: IEEE 30th International Conference on Data Engineering (ICDE), pp. 1254–1257 (2014)

    Google Scholar 

  45. Bracewell, R., et al.: Capturing design rationale. Computer-Aided Design 41(3), 173–186 (2009)

    Article  MathSciNet  Google Scholar 

  46. Hasegawa, R., et al.: Extracting Conceptual Graphs from Japanese Documents for Software Requirements Modeling. In: Proc. Sixth Asia-Pacific Conference on Conceptual Modelling (APCCM 2009), Wellington, New Zealand, pp. 87–96 (2009)

    Google Scholar 

  47. Di Maio, P.: ‘Just enough’ ontology engineering. In: Proc. International Conference on Web Intelligence, Mining and Semantics, WIMS 2011, pp. 8:1–8:10 (2011)

    Google Scholar 

  48. Wegeler, T., et al.: Evaluating the benefits of using domain-specific modeling languages: an experience report. In: Proc. 2013 ACM Workshop on Domain-Specific Modeling, pp. 7–12 (2013)

    Google Scholar 

  49. Karla, P.R., Gurupur, V.P.: C-PHIS: A Concept Map-Based Knowledge Base Framework to Develop Personal Health Information Systems. Journal of Medical System 37, 9970 (2013)

    Article  Google Scholar 

  50. Prabhakar, T.V., Kumar, K.: Design Decision Topology Model for Pattern Relationship Analysis. In: Asian PLOP 2010 Tokyo, pp. 16–17 (2010)

    Google Scholar 

  51. Wu, W. et al.: Formal Modeling of Airborne Software High-Level Requirements Based on Knowledge Graph. In: Buchmann, R., Kifor, C.V., Yu, J. (eds.) KSEM 2014. LNCS(LNAI), vol. 8793, Springer, Heidelberg, pp. 258–269 (2014)

    Google Scholar 

  52. Kakehi, M. et al.: Organization of Discussion Knowledge Graph from Collaborative Learning Record. In: Apolloni, B., Howlett, R.J. Jain, L. (eds.) KES 2007/WIRN 2007, Part III, LNCS(LNAI), vol. 4694, pp. 600–607. Springer, Heidelberg (2007)

    Google Scholar 

  53. Watanabe, Y. et al.: Organization of Solution Knowledge Graph from Collaborative Learning Records. In: Velásquez, J.D., Ríos, S.A., Howlett, R.J., Jain, L.C. (eds.) KES 2009, Part II. LNCS(LNAI), vol. 5712, pp. 564–571. Springer, Heidelberg (2009)

    Google Scholar 

  54. Scaniello, G. et al.: On the Effect of Using SysML Requirement Diagrams to Comprehend Requirements: Results from two controlled experiments. In: 18th International Conference on Evaluation and Assessment in Software Engineering, vol. 1, pp. 433–442 (2014)

    Google Scholar 

  55. Deufemia, V., et al.: Visually modelling data intensive web applications to assist end-user development. In: Proc. 6th International Symposium on Visual Information Communication and Interaction, pp. 17–26 (2013)

    Google Scholar 

  56. Kapec, P.: Visualizing software artifacts using hypergraphs. In: Proc. 26th Spring Conference on Computer Graphics, pp. 27–32 (2010)

    Google Scholar 

  57. Ploeger, B., Tankink, C.: Improving an Interactive Visualization of Transition Systems. In: Proc. 4th ACM Symposium on Software Visualization 2008, pp. 115–124. ACM (2008)

    Google Scholar 

  58. Kay, J., et al.: Visualisations for team learning: small teams working on long-term projects. In: Proc. 8th International Conference on Computer Supported Collaborative Learning, pp. 354–356 (2007)

    Google Scholar 

  59. Díaz, P., et al.: Visual representation of web design patterns for end-users. In: AVI 2008, Proc. Working Conference on Advanced Visual Interfaces, pp. 408–411 (2008)

    Google Scholar 

  60. Gerken, J., et al.: The concept maps method as a tool to evaluate the usability of APIs. In: Proc. SIGCHI Conference on Human Factors in Computing Systems, pp. 3373–3382 (2011)

    Google Scholar 

  61. Zhou, H., et al.: Developing Application Specific Ontology for Program Comprehension by Combining Domain Ontology with Code Ontology, Software Technol. Res. Lab., Montfort Univ., Leicester Quality Software (2008)

    Google Scholar 

  62. Espinosa-Curiel, I.E., et al.: Graphical Technique to Support the Teaching/Learning Process of Software Process Reference Models. In: Riel, A., O’Connor, R., Tichkiewitch, S., Messnarz, R. (eds.) EuroSPI 2010. CCIS, vol. 99, pp 13–24. Springer, Heidelberg (2010)

    Google Scholar 

  63. Chou, S.W., Young He, M.: Facilitating knowledge creation by knowledge assets. In: Proceedings of HICSS 37, p. 10. IEEE Computer Society (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alvaro Fernández Del Carpio .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Del Carpio, A.F., Angarita, L.B. (2015). Towards the Development of a Framework for Encouraging the Learning of SPICE Model by Using Knowledge Graphs. In: Rout, T., O’Connor, R., Dorling, A. (eds) Software Process Improvement and Capability Determination. SPICE 2015. Communications in Computer and Information Science, vol 526. Springer, Cham. https://doi.org/10.1007/978-3-319-19860-6_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-19860-6_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19859-0

  • Online ISBN: 978-3-319-19860-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics