Explore the Pilot
Research & Methodology

Research basis and value methodology

Published software-engineering research informs the effect hypotheses behind the FASAPP value model. Defined target-state scenarios translate those findings into reference values that can subsequently be tested against customer baselines.

01
Published research

What the research says

The evidence base spans software reuse, model-driven development, traceability and impact analysis, consistency in continuous delivery, and knowledge transfer across distributed delivery structures.

Software reuse

Industrial research shows that systematic reuse can reduce follow-on development effort and improve delivery economics when reusable foundations are deliberately created, maintained, and applied across comparable work.

Selected literature: Chen et al. (2024); Mohagheghi & Conradi (2007); Krüger & Berger (2020)

Model-driven and visual development

Empirical studies report productivity benefits for bounded development tasks when implementation work is shifted toward structured models, higher-level abstractions, or low-code development environments.

Selected literature: Gao et al. (2026); Varajão et al. (2023); Kamma & Sasi Kumar (2014)

Traceability and impact analysis

Research on traceability shows that explicit relationships between artifacts can improve the quality and efficiency of change-impact analysis and reduce the effort required to understand downstream effects.

Selected literature: Mäder & Egyed (2015)

Consistency and continuous delivery

Research on consistency management and DevOps traceability highlights the value of keeping related development artifacts aligned and preserving the provenance of technical changes across evolving systems.

Selected literature: Jongeling et al. (2022); Pauzi et al. (2023)

Distributed delivery and knowledge transfer

Empirical work on distributed and multi-party software delivery documents recurring coordination and knowledge-transfer challenges when system understanding remains fragmented across teams, suppliers, or organizational boundaries.

Selected literature includes public-sector and distributed-delivery research from the study evidence base.
02
Evidence types

Published evidence, model values, calculations, and customer results

The methodology keeps these number types distinct so that evidence from external publications, modeled reference scenarios, mathematical translations, and customer-specific measurements are interpreted correctly.

Type Meaning Use
EXTERNAL Evidence or figures reported by an external publication. Supports empirical context and the expected direction of effect.
MODEL Scenario values derived for a defined unit of frontend delivery work at mature product capability. Provides comparable FASAPP reference values for fit-qualified scenarios.
CALC A mathematical translation of another documented input. Used where a published or modeled value is converted into another representation.
CUSTOMER Values established against a documented customer baseline. Used to evaluate observed outcomes in a real delivery context.
03
Value methodology

From published evidence to testable reference scenarios

The public methodology shows how research is used to frame defined frontend delivery scenarios, derive reference values for mature product capability, and prepare those values for customer validation.

1 Published research

Evidence on reuse, productivity, traceability, consistency, and knowledge transfer.

2 Defined frontend delivery scenarios

Bounded units of work for which the relevant delivery effect can be evaluated.

3 Reference value model

Reference values derived for fit-qualified scenarios at mature product capability.

4 Customer baseline validation

Observed effort is compared with a documented baseline in a real delivery context.

Public methodology scope. Detailed internal analytical structures and calculation artifacts used to construct the complete value model are not published on this page.
04
Reference values

Expected effects at mature product capability

The public reference values summarize the expected reduction in active effort across four recurring frontend delivery scenarios.

Number type: MODEL
Business case Reference Reference Basis
Portfolio Change EfficiencyCHANGE ~30% Less active effort for addressable recurring frontend changes
New Module DeliveryBUILD ~30% Less active effort to reach a review-ready frontend baseline
Controlled Rollout and ReuseSCALE ~40% Less addressable follow-on effort for a suitable rollout
Regulated Change GovernanceGOVERN ~20-25% Less active effort for defined technical impact and evidence work

Interpretation

The reference values describe the expected reduction in active effort within clearly defined, addressable frontend delivery scenarios at mature product capability. Customer-specific outcomes are established against an actual baseline.

05
Customer validation

Reference scenarios become customer evidence through measurement

A pilot establishes the baseline for a selected delivery scenario, applies FASAPP to the same class of work, and compares the observed result with the documented baseline and reference model.

  1. 01
    Baseline

    Document the current process, scope, and active effort for the selected unit of work.

  2. 02
    Apply

    Use FASAPP within the agreed scenario and operational constraints.

  3. 03
    Measure

    Record the observed effort and relevant quality or delivery conditions.

  4. 04
    Compare

    Evaluate the observed customer result against the documented baseline and MODEL reference.

Explore the pilot
06
Selected references

Public research used in the evidence base

The references below form the external evidence base used in the Value Impact Study.

  1. Gao, D., Fagerholm, F. & Toivanen, V. (2026).What does current research say about the viability of low-code development? A systematic literature review. Journal of Systems and Software, 239, Article 112893. DOI: 10.1016/j.jss.2026.112893.
  2. Bucaioni, A., Cicchetti, A. & Ciccozzi, F. (2022).Modelling in low-code development: a multi-vocal systematic review. Software and Systems Modeling, 21, 1959-1981. DOI: 10.1007/s10270-021-00964-0.
  3. Varajão, J., Trigo, A. & Almeida, M. (2023).Low-code Development Productivity: Is winter coming for code-based technologies? Queue, 21(5), 87-107. DOI: 10.1145/3631183.
  4. Kamma, D. & Sasi Kumar, G. (2014).Effect of Model Based Software Development on Productivity of Enhancement Tasks – An Industrial Study. 21st Asia-Pacific Software Engineering Conference, 71-77. DOI: 10.1109/APSEC.2014.20.
  5. Bjarnason, E., Lang, F. & Mjöberg, A. (2023).An empirically based model of software prototyping: a mapping study and a multi-case study. Empirical Software Engineering, 28, Article 115. DOI: 10.1007/s10664-023-10331-w.
  6. Chen, X., Usman, M. & Badampudi, D. (2024).Understanding and evaluating software reuse costs and benefits from industrial cases – A systematic literature review. Information and Software Technology, 171, 107451. DOI: 10.1016/j.infsof.2024.107451.
  7. Mohagheghi, P. & Conradi, R. (2007).Quality, productivity and economic benefits of software reuse: a review of industrial studies. Empirical Software Engineering, 12(5), 471-516. DOI: 10.1007/s10664-007-9040-x.
  8. Krüger, J. & Berger, T. (2020).An Empirical Analysis of the Costs of Clone- and Platform-Oriented Software Reuse. ESEC/FSE 2020, 432-444. DOI: 10.1145/3368089.3409684.
  9. Mäder, P. & Egyed, A. (2015).Do developers benefit from requirements traceability when evolving and maintaining a software system? Empirical Software Engineering, 20(2), 413-441. DOI: 10.1007/s10664-014-9314-z.
  10. Schneider, S., Díaz Ferreyra, N. E., Quéval, P.-J., Simhandl, G., Zdun, U. & Scandariato, R. (2024).How Dataflow Diagrams Impact Software Security Analysis: an Empirical Experiment. SANER 2024, 952-963. DOI: 10.1109/SANER60148.2024.00103.
  11. Tian, F., Wang, T., Liang, P., Wang, C., Khan, A. A. & Babar, M. A. (2021).The impact of traceability on software maintenance and evolution: a mapping study. arXiv:2108.02133.
  12. Jongeling, R., Ciccozzi, F., Carlson, J. & Cicchetti, A. (2022).Consistency management in industrial continuous model-based development settings: a reality check. Software and Systems Modeling, 21, 1511-1530. DOI: 10.1007/s10270-022-01000-5.
  13. Pauzi, Z., Thind, R. & Capiluppi, A. (2023).Artifact Traceability in DevOps: An Industrial Experience Report. EASE 2023, 180-183. DOI: 10.1145/3593434.3593451.
  14. Ajimati, M. O., Carroll, N. & Maher, M. (2025).Adoption of low-code and no-code development: A systematic literature review and future research agenda. Journal of Systems and Software, 222, Article 112300. DOI: 10.1016/j.jss.2024.112300.
  15. Käss, S., Strahringer, S. & Westner, M. (2023).A Multiple Mini Case Study on the Adoption of Low Code Development Platforms in Work Systems. IEEE Access, 11, 118762-118786. DOI: 10.1109/ACCESS.2023.3325092.
  16. Stegmann, L., Berendorf, R., Zimmermann, S. K. & Weeger, A. (2024).Balancing Risks and Opportunities in the Governance of Low Code Development Platforms. Proceedings of the 32nd European Conference on Information Systems.
  17. Forsgren, N., Storey, M.-A., Maddila, C., Zimmermann, T., Houck, B. & Butler, J. (2021).The SPACE of Developer Productivity. Queue, 19(1), 20-48. DOI: 10.1145/3454122.3454124.
  18. DORA / Google Cloud (2024).Accelerate State of DevOps 2024 Report.
  19. Herbsleb, J. D. & Mockus, A. (2003).An Empirical Study of Speed and Communication in Globally Distributed Software Development. IEEE Transactions on Software Engineering, 29(6), 481-494. DOI: 10.1109/TSE.2003.1205177.
  20. Lassenius, C., Mohagheghi, P. & Seide Molléri, J. (2025).Bringing it home: successful backsourcing of software development in the public sector. Empirical Software Engineering, 30, Article 170. DOI: 10.1007/s10664-025-10722-1.

The references above form the external evidence base used in the Value Impact Study. They inform research findings, benchmark context, measurement choices, realization conditions, and methodological boundaries. Proprietary model-construction and source-mapping logic is not published.