New Techniques

  • Miroslaw Staron
  • Wilhelm Meding


Measurement, as a discipline, accompanied other software development activities from the beginning of the discipline. Since the beginning, new measurement theories, methods and tools have been developed to accompany the rapid development of the field of software engineering. Today, the main trends which shape the development of the discipline of measurement are (1) availability of large data sets, (2) availability of off-the-shelf machine learning tools, and (3) research in measurement reference etalons. In this chapter, we discuss these three trends, and describe the most prominent techniques useful for the discipline of measurement.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Abr10.
    Alain Abran. Software metrics and software metrology. John Wiley & Sons, 2010.Google Scholar
  2. AMdM+16.
    Nicolli SR Alves, Thiago S Mendes, Manoel G de Mendonça, Rodrigo O Spínola, Forrest Shull, and Carolyn Seaman. Identification and management of technical debt: A systematic mapping study. Information and Software Technology, 70:100–121, 2016.Google Scholar
  3. ASD+15.
    Harry Altinger, Sebastian Siegl, Dajsuren, Yanja, and Franz Wotawa. A novel industry grade dataset for fault prediction based on model-driven developed automotive embedded software. In 12th Working Conference on Mining Software Repositories (MSR). MSR 2015, 2015.Google Scholar
  4. ASM+14.
    Vard Antinyan, Miroslaw Staron, Wilhelm Meding, Per Österström, Erik Wikstrom, Johan Wranker, Anders Henriksson, and Jörgen Hansson. Identifying risky areas of software code in agile/lean software development: An industrial experience report. In Software Maintenance, Reengineering and Reverse Engineering (CSMR-WCRE), 2014 Software Evolution Week-IEEE Conference on, pages 154–163. IEEE, 2014.Google Scholar
  5. BEBH15.
    Laure Berti-Equille and Javier Borge-Holthoefer. Veracity of data: From truth discovery computation algorithms to models of misinformation dynamics. Synthesis Lectures on Data Management, 7(3):1–155, 2015.Google Scholar
  6. BGS+11.
    Dhruba Borthakur, Jonathan Gray, Joydeep Sen Sarma, Kannan Muthukkaruppan, Nicolas Spiegelberg, Hairong Kuang, Karthik Ranganathan, Dmytro Molkov, Aravind Menon, Samuel Rash, et al. Apache Hadoop goes realtime at Facebook. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of data, pages 1071–1080. ACM, 2011.Google Scholar
  7. Bos16.
    Jan Bosch. Speed, data, and ecosystems: The future of software engineering. IEEE Software, 33(1):82–88, 2016.Google Scholar
  8. CMB70.
    Peter W. Carey, Jacques Mehler, and Thomas G. Bever. Judging the veracity of ambiguous sentences. Journal of Verbal Learning and Verbal Behavior, 9(2):243–254, Apr 1970.Google Scholar
  9. FB14.
    Norman Fenton and James Bieman. Software metrics: A rigorous and practical approach. CRC Press, 2014.Google Scholar
  10. FDGLDG14.
    Marta Fernández-Diego and Fernando González-Ladrón-De-Guevara. Potential and limitations of the ISBSG dataset in enhancing software engineering research: A mapping review. Information and Software Technology, 56(6):527–544, 2014.Google Scholar
  11. FP98.
    Norman E Fenton and Shari Lawrence Pfleeger. Software metrics: A rigorous and practical approach. PWS Publishing Co., 1998.Google Scholar
  12. Gil77.
    Tom Gilb. Software metrics. Winthrop, 1977.Google Scholar
  13. Har12.
    Peter Harrington. Machine learning in action, volume 5. Manning Greenwich, CT, 2012.Google Scholar
  14. KA07a.
    Adel Khelifi and Alain Abran. Design steps for developing software measurement standard etalons for ISO 19761. In WSEAS International Conference on COMPUTERS, 2007.Google Scholar
  15. KA07b.
    Adel Khelifi and Alain Abran. Software measurement standard etalons: A design process. International Journal of Computers, 1, 2007.Google Scholar
  16. KLG15.
    Marina Krotofil, Jason Larsen, and Dieter Gollmann. The Process Matters. In Proceedings of the 10th ACM Symposium on Information Computer and Communications Security – ASIA CCS. Association for Computing Machinery (ACM), 2015.Google Scholar
  17. KNO12.
    Philippe Kruchten, Robert L Nord, and Ipek Ozkaya. Technical debt: From metaphor to theory and practice. Ieee software, 29(6):18–21, 2012.Google Scholar
  18. Lan13.
    Brett Lantz. Machine learning with R. Packt Publishing Ltd, 2013.Google Scholar
  19. LM15.
    Eveliina Lindgren and Jürgen Münch. Software development as an experiment system: A qualitative survey on the state of the practice. In International Conference on Agile Software Development, pages 117–128. Springer, 2015.Google Scholar
  20. LPM99.
    Timothy R. Levine, Hee Sun Park, and Steven A. McCornack. Accuracy in detecting truths and lies: Documenting the “veracity effect”. Communication Monographs, 66(2):125–144, Jun 1999.Google Scholar
  21. MB15.
    Antonio Martini and Jan Bosch. The danger of architectural technical debt: Contagious debt and vicious circles. In Software Architecture (WICSA), 2015 12th Working IEEE/IFIP Conference on, pages 1–10. IEEE, 2015.Google Scholar
  22. MBC14.
    Antonio Martini, Jan Bosch, and Michel Chaudron. Architecture technical debt: Understanding causes and a qualitative model. In Software Engineering and Advanced Applications (SEAA), 2014 40th EUROMICRO Conference on, pages 85–92. IEEE, 2014.Google Scholar
  23. MV06.
    Samantha Mann and Aldert Vrij. Police officers’ judgements of veracity tenseness, cognitive load and attempted behavioural control in real-life police interviews. Psychology, Crime & Law, 12(3):307–319, Jun 2006.Google Scholar
  24. OC07.
    International Standard Organization and International Electrotechnical Commission. Software and systems engineering, software measurement process. Technical report, ISO/IEC, 2007.Google Scholar
  25. oWM93.
    International Bureau of Weights and Measures. International vocabulary of basic and general terms in metrology. International Organization for Standardization, Geneva, Switzerland, 2nd edition, 1993.Google Scholar
  26. Qui14.
    J Ross Quinlan. C4.5: programs for machine learning. Elsevier, 2014.Google Scholar
  27. SM09.
    Miroslaw Staron and Wilhelm Meding. Ensuring reliability of information provided by measurement systems. In Software Process and Product Measurement, pages 1–16. Springer, 2009.Google Scholar
  28. SMNS16.
    Miroslaw Staron, Wilhelm Meding, Kent Niesel, and Ola Söder. Evolution of the role of measurement systems in industrial decision support. In Handbook of Research on Global Supply Chain Management, pages 560–580. IGI Global, 2016.Google Scholar
  29. SS16.
    Miroslaw Staron and Riccardo Scandariato. Data veracity in intelligent transportation systems: the slippery road warning scenario. In Intelligent Vehicles Symposium, 2016.Google Scholar
  30. Too14.
    Joris Toonders. Data is the new oil of the digital economy. Wired. (accessed 17 August 2017), 2014.
  31. YMM+17.
    Sezin Gizem Yaman, Myriam Munezero, Jürgen Münch, Fabian Fagerholm, Ossi Syd, Mika Aaltola, Christina Palmu, and Tomi Männistö. Introducing continuous experimentation in large software-intensive product and service organizations. Journal of Systems and Software, 2017.Google Scholar
  32. ZXW+16.
    Matei Zaharia, Reynold S Xin, Patrick Wendell, Tathagata Das, Michael Armbrust, Ankur Dave, Xiangrui Meng, Josh Rosen, Shivaram Venkataraman, Michael J Franklin, et al. Apache Spark: A unified engine for big data processing. Communications of the ACM, 59(11):56–65, 2016.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Miroslaw Staron
    • 1
  • Wilhelm Meding
    • 2
  1. 1.Department of Computer Science and EngineeringUniversity of GothenburgGothenburgSweden
  2. 2.Ericsson ABGothenburgSweden

Personalised recommendations