skip to main content
10.1145/3319008.3319010acmotherconferencesArticle/Chapter ViewAbstractPublication PageseaseConference Proceedingsconference-collections
research-article

Model-Integrated Queries for the Analysis of Runtime Events: A Controlled Experiment

Authors Info & Claims
Published:15 April 2019Publication History

ABSTRACT

Models describe a software system on an abstraction level higher than its actual implementation. Recent research results show that bringing models and a running system closer together by establishing traceability links between recorded runtime events and corresponding model elements improves the analysis performance of human observers when assessing the behaviour of the running system. Despite these results, common techniques for analyzing runtime events are rarely integrated into the models that are used for assessing the system behaviour from a high-level perspective. This paper presents a controlled experiment where model-integrated analysis facilities are compared with a more traditional analysis approach based on SQL queries to a system's database in terms of correctness and completion time of analysis tasks. The results show that model-integrated analyses allow analysts to give more correct answers to questions about the system behaviour, but provide no improvement of the time spent for completing the analysis tasks.

References

  1. Gordon Blair, Nelly Bencomo, and Robert B. France. 2009. [email protected]. Computer 42, 10 (Oct. 2009), 22--27. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. James Cheney, Sam Lindley, and Philip Wadler. 2013. A Practical Theory of Language-integrated Query. In Proceedings of the 18th ACM SIGPLAN International Conference on Functional Programming (ICFP '13). ACM, New York, NY, USA, 403--416. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Norman Cliff. 1996. Ordinal Methods for Behavioral Data Analysis. Erlbaum. https://books.google.at/books?id=bIJFvgAACAAJGoogle ScholarGoogle Scholar
  4. Thomas D. Cook and D.T. Campbell. 1979. Quasi-experimentation: design & analysis issues for field settings. Rand McNally College. https://books.google.at/books?id=68HynQEACAAJGoogle ScholarGoogle Scholar
  5. Alan J. Demers, Johannes Gehrke, Biswanath Panda, Mirek Riedewald, Varun Sharma, and Walker M. White. 2007. Cayuga: A General Purpose Event Monitoring System. In CIDR'07. 412--422.Google ScholarGoogle Scholar
  6. Françoise Fabret, H. Arno Jacobsen, François Llirbat, Joăo Pereira, Kenneth A. Ross, and Dennis Shasha. 2001. Filtering Algorithms and Implementation for Very Fast Publish/Subscribe Systems. SIGMOD Rec. 30, 2 (May 2001), 115--126. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Ronald Aylmer Fisher. 1925. Statistical Methods For Research Workers. Edinburgh Oliver & Boyd.Google ScholarGoogle Scholar
  8. William Sealy Gosset. 1908. The Probable Error of a Mean. Biometrika 6, 1 (March 1908), 1--25. Originally published under the pseudonym "Student".Google ScholarGoogle ScholarCross RefCross Ref
  9. Carmine Gravino, Giuseppe Scanniello, and Genoveffa Tortora. 2015. Source-code Comprehension Tasks Supported by UML Design Models. J. Vis. Lang. Comput. 28, C (June 2015), 23--38. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Thomas Haitzer and Uwe Zdun. 2013. Controlled Experiment on the Supportive Effect of Architectural Component Diagrams for Design Understanding of Novice Architects. In Proceedings of the 7th European Conference on Software Architecture (ECSA'13). Springer-Verlag, Berlin, Heidelberg, 54--71. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. John Hutchinson, Jon Whittle, Mark Rouncefield, and Steinar Kristoffersen. 2011. Empirical assessment of MDE in industry. In Software Engineering (ICSE), 2011 33rd International Conference on. 471--480. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Jaakko Järvi and John Freeman. 2010. C++ Lambda Expressions and Closures. Sci. Comput. Program. 75, 9 (Sept. 2010), 762--772. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Muhammad Atif Javed and Uwe Zdun. 2014. The Supportive Effect of Traceability Links in Architecture-Level Software Understanding: Two Controlled Experiments. In Software Architecture (WICSA), 2014IEEE/IFIP Conference on. 215--224. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Dileepa Jayathilake. 2012. Towards structured log analysis. In Computer Science and Software Engineering (JCSSE), 2012 International Joint Conference on. 259--264.Google ScholarGoogle ScholarCross RefCross Ref
  15. Barbara A. Kitchenham, Lech Madeyski, David Budgen, Jacky Keung, Pearl Brereton, Stuart Charters, Shirley Gibbs, and Amnart Pohthong. 2017. Robust Statistical Methods for Empirical Software Engineering. Empirical Software Engineering 22, 2 (01 Apr 2017), 579--630. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Barbara A. Kitchenham, Shari Lawrence Pfleeger, Lesley M. Pickard, Peter W. Jones, David C. Hoaglin, Khaled El Emam, and Jarrett Rosenberg. 2002. Preliminary Guidelines for Empirical Research in Software Engineering. IEEE Trans. Softw. Eng. 28, 8 (Aug. 2002), 721--734. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Henry B. Mann and Donald R. Whitney. 1947. On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other. Ann. Math. Statist. 18, 1 (03 1947), 50--60.Google ScholarGoogle Scholar
  18. HaiTao Mei, Ian Gray, and Andy Wellings. 2016. Real-Time Stream Processing in Java. In Reliable Software Technologies -- Ada-Europe 2016, Marko Bertogna, Luis Miguel Pinho, and Eduardo Quiñones (Eds.). Springer International Publishing, Cham, 44--57. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Rajeev Motwani, Jennifer Widom, Arvind Arasu, Brian Babcock, Shivnath Babu, Mayur Datar, Gurmeet Manku, Chris Olston, Justin Rosenstein, and Rohit Varma. 2002. Query Processing, Resource Management, and Approximation in a Data Stream Management System. Technical Report 2002--41. Stanford InfoLab. http://ilpubs.stanford.edu:8090/549/Google ScholarGoogle Scholar
  20. Cornelis Joost van Rijsbergen. 1979. Information Retrieval (2nd ed.). Butterworth-Heinemann, Newton, MA, USA.Google ScholarGoogle Scholar
  21. Mojtaba Shahin, Peng Liang, and Mohammad Reza Khayyambashi. 2010. Rationale Visualization of Software Architectural Design Decision Using Compendium. In Proceedings of the 2010 ACM Symposium on Applied Computing (SAC '10). ACM, New York, NY, USA, 2367--2368. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Mojtaba Shahin, Peng Liang, and Zengyang Li. 2011. Architectural Design Decision Visualization for Architecture Design: Preliminary Results of a Controlled Experiment. In Proceedings of the 5th European Conference on Software Architecture: Companion Volume (ECSA '11). ACM, New York, NY, USA, Article 2, 8 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Michael Szvetits and Uwe Zdun. 2015. Reusable event types for models at runtime to support the examination of runtime phenomena. In 2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS). 4--13.Google ScholarGoogle ScholarCross RefCross Ref
  24. Michael Szvetits and Uwe Zdun. 2016. Controlled Experiment on the Comprehension of Runtime Phenomena Using Models Created at Design Time. In Proceedings of the ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems (MODELS 16). ACM, New York, NY, USA, 151--161. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Huy Tran, Uwe Zdun, and Schahram Dustdar. 2011. VbTrace: using view-based and model-driven development to support traceability in process-driven SOAs. Software & Systems Modeling 10, 1 (2011), 5--29. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Jon Whittle, John Hutchinson, and Mark Rouncefield. 2014. The State of Practice in Model-Driven Engineering. IEEE Software 31, 3 (May 2014), 79--85.Google ScholarGoogle ScholarCross RefCross Ref
  27. Claes Wohlin, Martin Höst, and Kennet Henningsson. 2003. Empirical Research Methods in Software Engineering. In Empirical Methods and Studies in Software Engineering, Reidar Conradi and AlfInge Wang (Eds.). Lecture Notes in Computer Science, Vol. 2765. Springer Berlin Heidelberg, 7--23.Google ScholarGoogle Scholar
  28. Eugene Wu, Yanlei Diao, and Shariq Rizvi. 2006. High-performance Complex Event Processing over Streams. In Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data (SIGMOD '06). ACM, New York, NY, USA, 407--418. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Detlef Zimmer and Rainer Unland. 1999. On the semantics of complex events in active database management systems. In Data Engineering, 1999. Proceedings., 15th International Conference on. 392--399. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Model-Integrated Queries for the Analysis of Runtime Events: A Controlled Experiment

      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
      • Published in

        cover image ACM Other conferences
        EASE '19: Proceedings of the 23rd International Conference on Evaluation and Assessment in Software Engineering
        April 2019
        345 pages
        ISBN:9781450371452
        DOI:10.1145/3319008

        Copyright © 2019 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: 15 April 2019

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article
        • Research
        • Refereed limited

        Acceptance Rates

        EASE '19 Paper Acceptance Rate20of73submissions,27%Overall Acceptance Rate71of232submissions,31%
      • Article Metrics

        • Downloads (Last 12 months)0
        • Downloads (Last 6 weeks)0

        Other Metrics

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader