skip to main content
research-article

Assessing the Effect of Screen Mockups on the Comprehension of Functional Requirements

Published:07 October 2014Publication History
Skip Abstract Section

Abstract

Over the last few years, the software engineering community has proposed a number of modeling methods to represent functional requirements. Among them, use cases are recognized as an easy to use and intuitive way to capture and define such requirements. Screen mockups (also called user-interface sketches or user interface-mockups) have been proposed as a complement to use cases for improving the comprehension of functional requirements. In this article, we aim at quantifying the benefits achievable by augmenting use cases with screen mockups in the comprehension of functional requirements with respect to effectiveness, effort, and efficiency. For this purpose, we conducted a family of four controlled experiments, involving 139 participants having different profiles. The experiments involved comprehension tasks performed on the requirements documents of two desktop applications. Independently from the participants' profile, we found a statistically significant large effect of the presence of screen mockups on both comprehension effectiveness and comprehension task efficiency. No significant effect was observed on the effort to complete tasks. The main pragmatic lesson is that the screen mockups addition to use cases is able to almost double the efficiency of comprehension tasks.

Skip Supplemental Material Section

Supplemental Material

References

  1. S. Abrahão, C. Gravino, E. Insfran, G. Scanniello, and G. Tortora. 2013. Assessing the effectiveness of sequence diagrams in the comprehension of functional requirements: Results from a family of five experiments. IEEE Trans. Softw. Eng. 29, 3, 327--342. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. S. Adolph, A. Cockburn, and P. Bramble. 2002. Patterns for Effective Use Cases. Addison-Wesley Longman Publishing Co., Inc., Boston, MA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. A. Agresti. 2007. An Introduction to Categorical Data Analysis. Wiley-Interscience.Google ScholarGoogle Scholar
  4. B. Anda, D. I. K. Sjøberg, and M. Jørgensen. 2001. Quality and understandability of use case models. In Proceedings of the European Conference on Object-Oriented Programming. Berlin, Springer-Verlag, 402--428. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. G. Andrew and P. Drew. 2009. Use case diagrams in support of use case modeling: Deriving understanding from the picture. J. Datab. Manage. 20, 1, 1--24.Google ScholarGoogle ScholarCross RefCross Ref
  6. J. Aranda, N. Ernst, J. Horkoff, and S. Easterbrook. 2007. A framework for empirical evaluation of model comprehensibility. In Proceedings of the ICSE Workshop on Modeling in Software Engineering. IEEE, 7--13. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. E. Astesiano, M. Cerioli, G. Reggio, and F. Ricca. 2007. Phased highly-interactive approach to teaching UML-based software development. In Proceedings of the Symposium at MODELS 2007. 9--19.Google ScholarGoogle Scholar
  8. E. Astesiano and G. Reggio. 2012. A disciplined style for use case based requirement specification. Tech. Rep. DISI-TR-12-04. DISI University of Genova, Italy. http://softeng.disi.unige.it/TR/Disciplined Requirements.pdf.Google ScholarGoogle Scholar
  9. V. Basili, G. Caldiera, and D. H. Rombach. 1994. The Goal Question Metric Paradigm, Encyclopedia of Software Engineering. Wiley.Google ScholarGoogle Scholar
  10. V. Basili, F. Shull, and F. Lanubile. 1999. Building knowledge through families of experiments. IEEE Trans. Softw. Eng. IEEE, 456--473. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. L. Bratthall and C. Wohlin. 2002. Is it possible to decorate graphical software design and architecture models with qualitative information?-An Experiment. IEEE Trans. Softw. Eng. 28, 12, 1181--1193. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. B. Bruegge and A. H. Dutoit. 2003. Object-Oriented Software Engineering: Using UML, Patterns and Java, 2nd Ed. Prentice-Hall. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. J. Carver, L. Jaccheri, S. Morasca, and F. Shull. 2003. Issues in using students in empirical studies in software engineering education. In Proceedings of the International Symposium on Software Metrics. IEEE Computer Society, Los Alamitos, CA, 239--250. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. M. Ceccato, P. Di Penta, P. Falcarin, F. Ricca, M. Torchiano, and P. Tonella. 2014. A family of experiments to assess the effectiveness and efficiency of source code obfuscation techniques. Empir. Softw. Eng. 19, 4, 1040--1074. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. M. Ciolkowski, D. Muthig, and J. Rech. 2004. Using academic courses for empirical validation of software development processes. In Proceedings of the EUROMICRO Conference. IEEE Computer Society, Los Alamitos, CA, 354--361. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. N. Cliff. 1993. Dominance statistics: Ordinal analyses to answer ordinal questions. Psychol. Bull. 114, 3, 494--509.Google ScholarGoogle ScholarCross RefCross Ref
  17. A. Cockburn. 2000. Writing Effective Use Cases. Addison-Wesley, Reading, MA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. J. Conallen. 2002. Building Web Applications with UML, Second Edition. Addison-Wesley, Reading, MA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. N. Condori-Fernandez, M. Daneva, K. Sikkel, R. Wieringa, O. Dieste, and O. Pastor. 2009. Research findings on empirical evaluation of requirements specifications approaches. In Proceedings of the Workshop on Requirements Engineering. Valparaiso University Press, 121--128.Google ScholarGoogle Scholar
  20. O. J. Dunn. 1961. Multiple comparisons among means. J. ASA, 56, 52--64.Google ScholarGoogle Scholar
  21. J. Ferreira, J. Noble, and R. Biddle. 2007. Agile Development Iterations and UI Design. In Proceedings of the Agile Conference (AGILE). 50--58. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. M. Fowler. 2003. UML Distilled: A Brief Guide to the Standard Object Modeling Language, 3rd Ed. Addison-Wesley Longman Publishing Co., Inc., Boston, MA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. C. Gravino, G. Scanniello, and G. Tortora. 2008. An empirical investigation on dynamic modeling in requirements engineering. In Proceedings of the Conference on Model Driven Engineering Languages and Systems. IEEE Computer Society, Los Alamitos, CA, 615--629. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. J. Hannay and M. Jørgensen. 2008. The role of deliberate artificial design elements in software engineering experiments. IEEE Trans. Softw. Eng. 34, 2, 242--259. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. H. R. Hartson and E. C. Smith. 1991. Rapid prototyping in human-computer interface development. Interacting with Computers 3, 1, 51--91. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. D. Hendrix, J. Cross, and S. Maghsoodloo. 2002. The effectiveness of control structure diagrams in source code comprehension activities. IEEE Trans. Softw. Eng. 28, 5, 463--477. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. J. J. Higgins. 2004. An Introduction to Modern Nonparametric Statistics. Brooks/Cole.Google ScholarGoogle Scholar
  28. L. Hochstein, V. R. Basili, M. V. Zelkowitz, J. K. Hollingsworth, and J. Carver. 2005. Combining self-reported and automatic data to improve programming effort measurement. ACM SIGSOFT Softw. Eng. Notes 30, 5, 356--365. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. K. Hogarty and J. Kromrey. 2001. We've been reporting some effect sizes: Can you guess what they mean? In Proceedings of the Annual Meeting of the American Educational Research Association.Google ScholarGoogle Scholar
  30. L. Huang and M. Holcombe. 2009. Empirical investigation towards the effectiveness of test first programming. Inf. Softw. Tech. 51, 1, 182--194. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. W. S. Humphrey. 1995. A Discipline for Software Engineering. Addison-Wesley Longman Publishing Co., Inc., Boston, MA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. ISO. 1991. Information technology--software product evaluation: Quality characteristics and guidelines for their use, ISO/IEC IS 9126, ISO, Geneva.Google ScholarGoogle Scholar
  33. ISO. 2000. Ergonomic requirements for office work with visual display terminals (VDTs) -- Part 9: Requirements for non-keyboard input devices. ISO 9241-11, ISO, Geneva, Switzerland.Google ScholarGoogle Scholar
  34. ISO. 2011. Systems and software engineering--Systems and software quality requirements and Evaluation (SQuaRE)--System and software quality models. ISO/IEC 25010, ISO, Geneva, Switzerland.Google ScholarGoogle Scholar
  35. I. Jacobson. 2004. Object-Oriented Software Engineering: A Use Case Driven Approach. Addison-Wesley Longman Publishing Co., Inc., Redwood City, CA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. N. Juristo and A. M. Moreno. 2001. Basics of Software Engineering Experimentation. Kluwer Academic Publishers, Englewood Cliffs, NJ. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. R. I. Kabacoff. 2011. R in Action -- Data Analysis and Graphics with R. Manning Publications Co., Shelter Island, NY. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. E. Kamsties, A. von Knethen, and R. Reussner. 2003. A controlled experiment to evaluate how styles affect the understandability of requirements specifications. Inf. Softw. Tech. 45, 14, 955--965.Google ScholarGoogle ScholarCross RefCross Ref
  39. B. Kitchenham, H. Al-Khilidar, M. Babar, M. Berry, K. Cox, J. Keung, F. Kurniawati, M. Staples, H. Zhang, and L. Zhu. 2008. Evaluating guidelines for reporting empirical software engineering studies. Empir. Softw. Eng. 13, 97--121. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. B. Kitchenham, S. Pfleeger, L. Pickard, P. Jones, D. Hoaglin, K. El Emam, and J. Rosenberg. 2002. Preliminary guidelines for empirical research in software engineering. IEEE Trans. Softw. Eng. 28, 8, 721--734. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. J. A. Landay and B. A. Myers. 1995. Interactive sketching for the early stages of user interface design. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM Press/Addison-Wesley Publishing Co., New York, NY, 43--50. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. S. Lauesen. 2002. Software Requirements: Styles and Techniques. Addison-Wesley. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. M. Lindvall, I. Rus, F. Shull, M. V. Zelkowitz, P. Donzelli, A. M. Memon, V. R. Basili, P. Costa, R. T. Tvedt, L. Hochstein, S. Asgari, C. Ackermann, and D. Pech. 2005. An evolutionary testbed for software technology evaluation. Innov. Syst. Softw. Eng. 1, 1, 3--11.Google ScholarGoogle ScholarCross RefCross Ref
  44. M. G. Mendonça, J. C. Maldonado, M. C. F. de Oliviera et al. 2008. A framework for software engineering experimental replications. In Proceedings of the 13th IEEE International Conference on Engineering of Complex Computer Systems (ICECCS'08). IEEE Computer Society, Los Alamitos, CA, 203--212. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. B. Meyer. 1985. On formalism in specification. IEEE Softw. 3, 1, 6--25. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. H. Motulsky. 2010. Intuitive Biostatistics: A Non-Mathematical Guide to Statistical Thinking. Oxford University Press.Google ScholarGoogle Scholar
  47. R. Mugridge and W. Cunningham. 2005. Fit for Developing Software: Framework for Integrated Tests. Prentice-Hall. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. M. O'Docherty. 2005. Object-Oriented Analysis and Design: Understanding System Development with UML 2.0 1st Ed. Wiley.Google ScholarGoogle Scholar
  49. A. N. Oppenheim. 1992. Questionnaire Design, Interviewing and Attitude Measurement. Pinter, London, UK.Google ScholarGoogle Scholar
  50. S. L. Pfleeger and W. Menezes. 2000. Marketing technology to software practitioners. IEEE Softw. 17, 1, 27--33. Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. K. T. Phalp, J. Vincent, and K. Cox. 2007. Improving the quality of use case descriptions: Empirical assessment of writing guidelines. Softw. Qual. Cont. 15, 4, 383--399. Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. R Core Team. 2013. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.Google ScholarGoogle Scholar
  53. F. Ricca, M. Di Penta, M. Torchiano, P. Tonella, and M. Ceccato. 2010a. How developers' experience and ability influence web application comprehension tasks supported by UML stereotypes: A Series of Four Experiments. IEEE Trans. Softw. Eng. 36, 96--118. Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. F. Ricca, G. Scanniello, M. Torchiano, G. Reggio, and E. Astesiano. 2010b. On the effectiveness of screen mockups in requirements engineering: results from an internal replication. In Proceedings of the International Symposium on Empirical Software Engineering and Measurement. ACM, New York, NY. Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. F. Ricca, G. Scanniello, M. Torchiano, G. Reggio, and E. Astesiano. 2010c. On the effort of augmenting use cases with screen mockups: results from a preliminary empirical study. In Proceedings of the International Symposium on Empirical Software Engineering and Measurement. ACM, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. F. Ricca, M. Torchiano, M. Di Penta, M. Ceccato, and P. Tonella. 2009. Using acceptance tests as a support for clarifying requirements: A series of experiments. Inf. Softw. Tech. 51, 2, 270--283. Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. J. Romano, J. D. Kromrey, J. Coraggio, and J. Skowronek. 2006. Appropriate statistics for ordinal level data: Should we really be using t-test and Cohen's d for evaluating group differences on the NSSE and other surveys? In Proceedings of the Annual Meeting of the Florida Association of Institutional Research.Google ScholarGoogle Scholar
  58. G. Scanniello, C. Gravino, M. Genero, J. A. Cruz-Lemus, and G. Tortora. 2014. On the impact of UML analysis models on source code comprehensibility and modifiability. Trans. Softw. Eng. Meth. 23, 2, 13:1--13:26. Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. G. Scanniello, C. Gravino, and G. Tortora. 2010. Investigating the role of UML in the software modeling and maintenance - A preliminary industrial survey. In Proceedings of ICEIS. 141--148.Google ScholarGoogle Scholar
  60. G. Scanniello, F. Ricca, M. Torchiano, C. Gravino, and G. Reggio. 2013. Estimating the effort to develop screen mockups. In Proceedings of the EUROMICRO Conference on Software Engineering and Advanced Applications. 341--348. Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. S. Shapiro and M. Wilk. 1965. An analysis of variance test for normality. Biometrika 52, 3--4, 591--611.Google ScholarGoogle ScholarCross RefCross Ref
  62. F. Shull, J. C. Carver, S. Vegas, and N. J. Juzgado. 2008. The role of replications in empirical software engineering. Empir. Softw. Eng. 13, 2, 211--218. Google ScholarGoogle ScholarDigital LibraryDigital Library
  63. M. Staron, L. Kuzniarz, and C. Wohlin. 2006. Empirical assessment of using stereotypes to improve comprehension of UML models: A set of experiments. J. Syst. Softw. 79, 5, 727--742. Google ScholarGoogle ScholarDigital LibraryDigital Library
  64. M. Torchiano. 2014. Effsize: Efficientc Effects Size Computation. R package version 1.2-2.Google ScholarGoogle Scholar
  65. M. Torchiano, F. C. A. Tomassetti, F. Ricca, A. Tiso, and G. Reggio. 2013. Relevance, benefits, and problems of software modelling and model driven techniques--A survey in the Italian industry. J. Syst. Softw. 86, 8, 2110--2126. Google ScholarGoogle ScholarDigital LibraryDigital Library
  66. H. van der Linden, G. Boers, H. Tange, J. Talmon, and A. Hasman. 2003. Proper: A multi disciplinary EPR system. Int. J. Med. Inf. 70, 2--3, 149--160.Google ScholarGoogle ScholarCross RefCross Ref
  67. B. Wheeler. 2010. lmPerm: Permutation Tests for Linear Models. R package version 1.1-2.Google ScholarGoogle Scholar
  68. C. Wohlin, P. Runeson, M. Höst, M. Ohlsson, B. Regnell, and A. Wesslén. 2012. Experimentation in Software Engineering. Springer. Google ScholarGoogle ScholarDigital LibraryDigital Library
  69. R. Young. 2001. Effective Requirements Practice. Addison-Wesley, Boston, MA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  70. T. Yue, L. C. Briand, and Y. Labiche. 2009. A use case modeling approach to facilitate the transition towards analysis models: Concepts and empirical evaluation. In Proceedings of the 12th International Conference on Model Driven Engineering Languages and Systems (MODELS'09). Springer-Verlag, Berlin, 484--498. Google ScholarGoogle ScholarDigital LibraryDigital Library
  71. M. K. Zimmerman, K. Lundqvist, and N. Leveson. 2002. Investigating the readability of state-based formal requirements specification languages. In Proceedings of the International Conference on Software Engineering. ACM, New York, 33--43. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Assessing the Effect of Screen Mockups on the Comprehension of Functional Requirements

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    • Published in

      cover image ACM Transactions on Software Engineering and Methodology
      ACM Transactions on Software Engineering and Methodology  Volume 24, Issue 1
      September 2014
      226 pages
      ISSN:1049-331X
      EISSN:1557-7392
      DOI:10.1145/2676679
      Issue’s Table of Contents

      Copyright © 2014 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 7 October 2014
      • Accepted: 1 March 2014
      • Revised: 1 September 2013
      • Received: 1 June 2013
      Published in tosem Volume 24, Issue 1

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader