Skip to main content

Lightweight Software Process Improvement Using Productivity and Sustainability Improvement Planning (PSIP)

Part of the Communications in Computer and Information Science book series (CCIS,volume 1190)


Productivity and Sustainability Improvement Planning (PSIP) is a lightweight, iterative workflow that allows software development teams to identify development bottlenecks and track progress to overcome them. In this paper, we present an overview of PSIP and how it compares to other software process improvement (SPI) methodologies, and provide two case studies that describe how the use of PSIP led to successful improvements in team effectiveness and efficiency.


  • Software development
  • Software engineering
  • Software process improvement

E. Gonsiorowski, R. Gupta, R. Milewicz, J. D. Moulton, G. R. Watson, J. Willenbring,

R. J. Zamora, E. M. Raybourn—contributed equally to this work.

R. J. Zamora—work was conducted while employed by Argonne National Laboratory.

Under the terms of Contract DE-NA0003525, there is a non-exclusive license for use of this work by or on behalf of the U.S. Government.

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-44728-1_6
  • Chapter length: 13 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   64.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-44728-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   84.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.


  1. 1.

  2. 2.

  3. 3.

  4. 4.

    See for further elaboration.

  5. 5.

  6. 6.

    Exascale Onboarding training portal:


  1. Basili, V.R., Caldiera, G.: Improve soft-ware quality by reusing knowledge and experience. Sloan Manag. Rev. 37, 55 (1995)

    Google Scholar 

  2. Baxter, S.M., Day, S.W., Fetrow, J.S., Reisinger, S.J.: Scientific software development is not an oxymoron. PLoS Comput. Biol. 2(9), e87 (2006)

    CrossRef  Google Scholar 

  3. Briand, L., El Emam, K., Melo, W.L.: ANSI-an inductive method for software process improvement: concrete steps and guidelines (1995)

    Google Scholar 

  4. Chrissis, M.B., Konrad, M., Shrum, S.: CMMI Guidelines for Process Integration and Product Improvement. Addison-Wesley Longman Publishing Co., Inc., Boston (2003)

    Google Scholar 

  5. Deming, W.E.: Elementary Principles of the Statistical Control of Quality: A Series of Lectures. Nippon Kegaku Gijutsu Remmei, Tokyo (1950)

    Google Scholar 

  6. Eisty, N.U., Thiruvathukal, G.K., Carver, J.C.: Use of software process in research software development: a survey. In: Proceedings of the Evaluation and Assessment on Software Engineering, pp. 276–282. ACM (2019)

    Google Scholar 

  7. Emam, K.E., Melo, W., Drouin, J.N.: SPICE: The Theory and Practice of Software Process Improvement and Capability Determination. IEEE Computer Society Press, Washington, D.C. (1997)

    MATH  Google Scholar 

  8. Heroux, M., et al.: Developer productivity and software sustainability report: advancing scientific productivity through better scientific software, September 2018

    Google Scholar 

  9. Holdren, J.P., Donovan, S.: National strategic computing initiative strategic plan. Technical report, National Strategic Computing Initiative Executive Council (2016)

    Google Scholar 

  10. Klünder, J., et al.: Catching up with method and process practice: an industry-informed baseline for researchers. In: Proceedings of the 41st International Conference on Software Engineering: Software Engineering in Practice, pp. 255–264. IEEE Press (2019)

    Google Scholar 

  11. Kuhrmann, M., Diebold, P., Münch, J.: Software process improvement: a systematic mapping study on the state of the art. PeerJ Comput. Sci. 2, e62 (2016)

    CrossRef  Google Scholar 

  12. McIlroy, M.: Software engineering: report on a conference sponsored by the NATO science committee. In: NATO Software Engineering Conference, NATO Scientific Affairs Division, pp. 138–155 (1968)

    Google Scholar 

  13. Mesh, E.S.: Supporting scientific SE process improvement. In: Proceedings of the 37th International Conference on Software Engineering-Volume 2, pp. 923–926. IEEE Press (2015)

    Google Scholar 

  14. Osterweil, L.: Software processes are software too. In: ICSE 1987: Proceedings of the 9th International Conference on Software Engineering, Monterey (1987)

    Google Scholar 

  15. Paulk, M.C., Curtis, B., Chrissis, M.B., Weber, C.V.: Capability maturity model, version 1.1. IEEE Softw. 10(4), 18–27 (1993)

    CrossRef  Google Scholar 

  16. Pettersson, F., Ivarsson, M., Gorschek, T., Öhman, P.: A practitioner’s guide to light weight software process assessment and improvement planning. J. Syst. Softw. 81(6), 972–995 (2008)

    CrossRef  Google Scholar 

  17. Pinto, G., Wiese, I., Dias, L.F.: How do scientists develop scientific software? An external replication. In: 25th International Conference on Software Analysis, Evolution and Reengineering, SANER 2018, Campobasso, Italy, 20–23 March 2018, pp. 582–591 (2018)

    Google Scholar 

  18. Stojanov, Z., Dobrilovic, D.: Learning in software process assessment based on feedback sessions outputs. In: Information Technology and Development of Education (ITRO) 2015, p. 259 (2015)

    Google Scholar 

  19. Stojanov, Z.: Inductive approaches in software process assessment. In: International Conference on Applied Internet and Information Technologies (2016)

    Google Scholar 

  20. Tell, P., et al.: What are hybrid development methods made of?: an evidence-based characterization. In: Proceedings of the International Conference on Software and System Processes, pp. 105–114. IEEE Press (2019)

    Google Scholar 

Download references


Special thanks to Lois McInnes (ANL) and the members of IDEAS-ECP. Thanks to PSIP partners Danny Perez (LANL), Art Voter (LANL), Christoph Junhans (LANL), and Pavan Balaji (ANL). Images used by permission.

This work was supported by the U.S. Department of Energy Office of Science, Office of Advanced Scientific Computing Research (ASCR), Office of Biological and Environmental Research (BER), and by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration.

This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2019-9693 C.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Elaine M. Raybourn .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2020 National Technology & Engineering Solutions of Sandia, LLC.

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Heroux, M.A. et al. (2020). Lightweight Software Process Improvement Using Productivity and Sustainability Improvement Planning (PSIP). In: Juckeland, G., Chandrasekaran, S. (eds) Tools and Techniques for High Performance Computing. HUST SE-HER WIHPC 2019 2019 2019. Communications in Computer and Information Science, vol 1190. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-44727-4

  • Online ISBN: 978-3-030-44728-1

  • eBook Packages: Computer ScienceComputer Science (R0)