http://iet.metastore.ingenta.com
1887

Empirical study on software process variability modelling with SMartySPEM and vSPEM

Empirical study on software process variability modelling with SMartySPEM and vSPEM

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Software — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

With the continuous improvement of software processes, it is possible to increase quality, to address different application domains and accelerate the development of software products. However, existing process modelling notations like the SPEM meta-model typically do not have appropriate constructs for expressing process variability. Thus, SPEM-based approaches such as SMartySPEM and vSPEM provide mechanisms for representing variabilities to address characteristics of different projects. Here, the authors empirically compared SMartySPEM and for variability representation in software processes models, aiming to analyze correction, time, and efficiency. The performed experiment provides preliminary evidence based on 11 participants and sample size N = 44. Participants took longer to comprehend diagrams in SMartySPEM. However, the correctness of SMartySPEM diagrams had a superior result and higher efficiency based on the median values and hypothesis tests. With regard to variability mechanisms, the diagrams modelled with SMartySPEM had a slightly lower efficiency and it took longer for modifying them, thus the correctness of the SMartySPEM diagrams had a superior result. Therefore, an initial body of knowledge indicated a positive efficiency of SMartySPEM for variability representation in process models, as well as potential improvements as the reduction in SMartySPEM diagrams complexity, which may contribute to its evolution.

References

    1. 1)
      • 1. Aleixo, F.A., Freire, M.A., dos Santos, W.C., et al: ‘Automating the variability management, customization and deployment of software processes: a model-driven approach’. Enterprise Information Systems, Berlin, Heidelberg, 2011(Lecture Notes in Business Information Processing, 73), pp. 372387.
    2. 2)
      • 2. Münch, J., Armbrust, O., Kowalczyk, M., et al: ‘Software process definition and management’, in ‘The Fraunhofer IESE series on software and systems engineering’ (Springer, Berlin, Heidelberg, 2012), pp. 1236.
    3. 3)
      • 3. Bendraou, R., Jezequel, J., Gervais, M.P., et al: ‘A comparison of six UML-based languages for software process modeling’, IEEE Trans. Softw. Eng., 2010, 36, (5), pp. 662675.
    4. 4)
      • 4. Carvalho, D.D., Chagas, L.F., Lima, A.M., et al: ‘Software process lines: a systematic literature review’. Int. Conf. Software Process Improvement and Capability Determination, Vilnius, Lithuania, 2014, pp. 118130.
    5. 5)
      • 5. Kalus, G., Kuhrmann, M.: ‘Criteria for software process tailoring: a systematic review’. Int. Conf. Software and System Process, San Francisco, CA, USA, 2013, pp. 171180.
    6. 6)
      • 6. Martínez-Ruiz, T., Münch, J., García, F., et al: ‘Requirements and constructors for tailoring software processes: a systematic literature review’, Softw. Qual. J., 2011, 20, (1), pp. 229260.
    7. 7)
      • 7. Basili, V.R., Rombach, H.D.: ‘Tailoring the software process to project goals and environments’. Int. Conf. Software Engineering, Monterey, CA, USA, 1987, pp. 345357.
    8. 8)
      • 8. Ruiz-Rube, I., Dodero, J.M., Palomo-Duarte, M., et al: ‘Uses and applications of SPEM process models: a systematic mapping study’, J. Softw. Maint. Evol. Res. Pract., 2012, 1, (32), pp. 9991025.
    9. 9)
      • 9. Dias, J.W., OliveiraJr, E.: ‘Modeling variability in software process with EPF composer and SMartySPEM: an empirical qualitative study’. Int. Conf. Enterprise Information Systems, 2016, pp. 283293.
    10. 10)
      • 10. Dias, J.W., OliveiraJr, E., Silva, M.A.G.: ‘Preliminary empirical evidence on SPrL variability management with EPF and SMartySPEM’. Brazilian Symp. Software Engineering, Maringa, Brazil, 2016, pp. 133142.
    11. 11)
      • 11. Aleixo, F.A., Kulesza, U., OliveiraJr, E.: in: Heidrich, J., Oivo, M., Jedlitschka, A., Baldassarre, M.T. (Eds.): ‘Modeling variabilities from software process lines with compositional and annotative techniques: a quantitative study’ (Springer, Berlin, Heidelberg, 2013), pp. 153168.
    12. 12)
      • 12. OliveiraJr, E., Pazin, M.G., Gimenes, I.M.S., et al: ‘SMartySPEM: a SPEM-based approach for variability management in software process lines’, in: ‘Product-focused software process improvement’, vol. 7983 (Springer, Berlin Heidelberg, 2013). pp. 169183.
    13. 13)
      • 13. Martínez-Ruiz, T., García, F., Piattini, M., et al: ‘Modelling software process variability: an empirical study’, IET Softw., 2011, 5, (2), pp. 172187.
    14. 14)
      • 14. OMG: ‘Software & systems process engineering meta-model specification (SPEM) – version 2.0’. (OMG, 2008). Available at http://www.omg.org/spec/SPEM, accessed 1 February 2017..
    15. 15)
      • 15. García-Borgoñón, L., Barcelona, M.A., García-García, J.A., et al: ‘Software process modeling languages: a systematic literature review’, Inf. Softw. Technol., 2014, 56, (2), pp. 103116.
    16. 16)
      • 16. Combemale, B., Crégut, X., Caplain, A., et al: ‘Towards a rigorous process modeling with SPEM’. Int. Conf. Enterprise Information Systems, Paphos, Cyprus, 2006, pp. 530533.
    17. 17)
      • 17. Washizaki, H.: ‘Deriving project-specific processes from process line architecture with commonality and variability’. Int. Conf. Industrial Informatics, Singapore, 2006, pp. 13011306.
    18. 18)
      • 18. Martínez-Ruiz, T., García, F., Piattini, M.: ‘Towards a SPEM v2.0 extension to define process lines variability mechanisms’, in ‘Software engineering research, management and applications’, vol. 150 (Springer, Berlin, Heidelberg, 2008), pp. 115130.
    19. 19)
      • 19. Bendraou, R., Combemale, B., Cregut, X., et al: ‘Definition of an executable SPEM 2.0’. Asia-Pacific Software Engineering Conf., Nanjing, China, 2007, pp. 390397.
    20. 20)
      • 20. Koudri, A., Champeau, J.: ‘MODAL: a SPEM extension to improve co-design process models’. New Modeling Concepts for Today's Software Processes, Beijing, China, 2010 (Lecture Notes in Computer Science, 6195), pp. 248259.
    21. 21)
      • 21. Ellner, R., Al-Hilank, S., Drexler, J., et al: ‘eSPEM: a SPEM extension for enactable behavior modeling’. Modelling Foundations and Applications, Paris, France, 2010 (Lecture Notes in Computer Science, 6138), pp. 116131.
    22. 22)
      • 22. Aoussat, F., Oussalah, M., Nacer, M.A.: ‘SPEM extension with software process architectural concepts’. Computer, Software and Applications Conf., Munich, Germany, 2011, pp. 215223.
    23. 23)
      • 23. Ternité, T.: ‘Process lines: a product line approach designed for process model development’. Euromicro Conf. Software Engineering and Advanced Applications, Patras, Greece, 2009, pp. 173180.
    24. 24)
      • 24. Kuhrmann, M., Fernández, D.M., Ternité, T.: ‘Realizing software process lines: insights and experiences’. Int. Conf. Software and System Process, Nanjing, China, 2014, pp. 99108.
    25. 25)
      • 25. Schramm, J., Dohrmann, P., Kuhrmann, M.: ‘Development of flexible software process lines with variability operations: a longitudinal case study’. Int. Conf. Evaluation and Assessment in Software Engineering, Nanjing, China, 2015, pp. 110.
    26. 26)
      • 26. Rombach, D.: ‘Integrated software process and product lines’. Unifying the Software Process Spectrum, Shanghai, China, 2006(Lecture Notes in Computer Science, 3840), pp. 8390.
    27. 27)
      • 27. Armbrust, O., Katahira, M., Miyamoto, Y., et al: ‘Scoping software process lines’, Softw. Process Improv. Pract.– Examining Process Design and Change, 2009, 14, (3), pp. 181197.
    28. 28)
      • 28. Alegría, J.A.H., Bastarrica, M.C.: ‘Building software process lines with CASPER’. Int. Conf. Software and System Process, Zurich, Switzerland, 2012, pp. 170179.
    29. 29)
      • 29. Jacobson, I., Booch, G., Rumbaugh, J.: ‘The unified software development process’, vol. 1 (Addison-Wesley Professional, Boston, MA, USA, 1999).
    30. 30)
      • 30. Zamli, K.Z., Lee, P.A.: ‘Taxonomy of process modeling languages’. IEEE Int. Conf. Computer Systems and Applications, Beirut, 2001, pp. 435437.
    31. 31)
      • 31. OliveiraJr, E., Gimenes, I.M.S., Maldonado, J.C.: ‘Systematic management of variability in UML-based software product lines’, J. Univers. Comput. Sci., 2010, 16, (17), pp. 23742393.
    32. 32)
      • 32. Martinez-Ruiz, T., Garcia, F., Piattini, M.: ‘Enhanced variability mechanisms to manage software process lines’. European Systems and Software Process Improvement and Innovation, Alcalá de Henares, Madrid, 2009. pp. 110.
    33. 33)
      • 33. Washizaki, H.: ‘Building software process line architectures from bottom up’. Product-Focused Software Process Improvement, Amsterdam, Netherlands, 2006 (Lecture Notes in Computer Science, 4034), pp. 415421.
    34. 34)
      • 34. Salman, I., Misirli, A.T., Juristo, N.: ‘Are students representatives of professionals in software engineering experiments?’. Int. Conf. Software Engineering, Firenze, Italy, vol. 1, 2015, pp. 666676.
    35. 35)
      • 35. Svahnberg, M., Aurum, A., Wohlin, C.: ‘Using students as subjects – an empirical evaluation’. Int. Symp. Empirical Software Engineering and Measurement, Kaiserslautern, Germany, 2008, pp. 288290.
    36. 36)
      • 36. Berander, P.: ‘Using students as subjects in requirements prioritization’. Int. Symp. Empirical Software Engineering, Redondo Beach, CA, USA, 2004, pp. 167176.
    37. 37)
      • 37. Daun, M., Salmon, A., Weyer, T., et al: ‘The impact of students’ skills and experiences on empirical results: a controlled experiment with undergraduate and graduate students’. Int. Conf. Evaluation and Assessment in Software Engineering, Nanjing, China, 2015, pp. 16.
    38. 38)
      • 38. Höst, M., Regnell, B., Wohlin, C.: ‘Using students as subjects – a comparative study of students and professionals in lead-time impact assessment’, Empir. Softw. Eng., 2000, 5, (3), pp. 201214.
    39. 39)
      • 39. Oktaba, H., Piattini, M., Pino, F., et al: ‘COMPETISOFT: an improvement strategy for small Latin-American Software Organizations’, in Oktaba, H., Alquicira, C., Pino, F.J., et al (Eds.): ‘Software process improvement for small and medium enterprises: techniques and case studies’ (IGI, Mexico City, Mexico, 2008), pp. 211222.
    40. 40)
      • 40. Neto, A.A., Conte, T.: ‘A conceptual model to address threats to validity in controlled experiments’. Int. Conf. Evaluation and Assessment in Software Engineering, Porto de Galinhas, Brazil, 2013, pp. 8285.
    41. 41)
      • 41. Wohlin, C., Runeson, P., Höst, M., et al: ‘Experimentation in software engineering: an introduction’ (Kluwer Academic Publishers, Norwell, MA, USA, 2000).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-sen.2017.0061
Loading

Related content

content/journals/10.1049/iet-sen.2017.0061
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address