Architecture and verification of a decision support system for software project quality in a continuous integration pipeline

A release decision – to admit a software version, return it for rework, or reallocate quality-control resources – is a basic management operation in a software development organization. For such decisions to be made reproducibly, the quality-management mechanism must be realized as an automated service embedded in the continuous integration and delivery pipeline. This paper presents a decision support system that implements a release-decision mechanism based on a non-compensatory integral quality assessment and is addressed to the release decision maker: the head of the quality assurance service or the technical lead of the project. A three-layer architecture with a detachable computational core, a five-entity relational data model that makes decisions traceable and scores comparable over time, and the management loop are described: computation of the integral score and subindices, a three-zone release-gate decision rule, temporal degradation triggers, metric-level drill-down recommendations, and an executable pipeline gate. A verification strategy is proposed in which the formal properties of the assessment aggregation are fixed as acceptance tests; their non-vacuity is confirmed by negative control and mutation testing, the implementation reproduces these properties at high code coverage, and the core exhibits linear complexity. The loop is demonstrated on a real-world anonymized project portfolio, tracing degradation from metrics to a management decision.

1. Giray G., Bennin K.E., Köksal Ö., Babur O., Tekinerdogan B. On the use of deep learning in software defect prediction. Journal of Systems and Software. 2023;195:111537. https://doi.org/10.1016/j.jss.2022.111537

2. Matloob F., Ghazal T.M., Taleb N., et al. Software Defect Prediction Using Ensemble Learning: A Systematic Literature Review. IEEE Access. 2021;9:98754–98771. https://doi.org/10.1109/ACCESS.2021.3095559

3. Kotsyuba I.Yu., Buyakova E.S. Methods and models for intelligent decision support in the management of software services for handling citizens' enquiries. Economic Bulletin. 2026;5(2):74–85. (In Russ.). https://doi.org/10.66621/2949-4648-2026-5-2-74-85

4. Donca I.-C., Stan O.P., Misaros M., Gota D., Miclea L. Method for Continuous Integration and Deployment Using a Pipeline Generator for Agile Software Projects. Sensors. 2022;22(12):4637. https://doi.org/10.3390/s22124637

5. Soares E., Sizilio G., Santos J., da Costa D.A., Kulesza U. The effects of continuous integration on software development: a systematic literature review. Empirical Software Engineering. 2022;27(3):78. https://doi.org/10.1007/s10664-021-10114-1

6. Biazotto J.P., Feitosa D., Avgeriou P., Nakagawa E.Y. Technical debt management automation: State of the art and future perspectives. Information and Software Technology. 2023;167(2):107375. https://doi.org/10.1016/j.infsof.2023.107375

7. Sas D., Avgeriou P. An Architectural Technical Debt Index Based on Machine Learning and Architectural Smells. IEEE Transactions on Software Engineering. 2023;49(8):4169–4195. https://doi.org/10.1109/TSE.2023.3286179

8. Ulan M., Löwe W., Ericsson M., Wingkvist A. Weighted software metrics aggregation and its application to defect prediction. Empirical Software Engineering. 2021;26(5):86. https://doi.org/10.1007/s10664-021-09984-2

9. Govindan K., Jepsen M.B. ELECTRE: A comprehensive literature review on methodologies and applications. European Journal of Operational Research. 2015;250(1). https://doi.org/10.1016/j.ejor.2015.07.019

10. Keshavarz-Ghorabaee M., Amiri M., Zavadskas E.K., Turskis Z., Antucheviciene J. Determination of Objective Weights Using a New Method Based on the Removal Effects of Criteria (MEREC). Symmetry. 2021;13(4):525. https://doi.org/10.3390/sym13040525

11. Mukhametzyanov I. Specific character of objective methods for determining weights of criteria in MCDM problems: Entropy, CRITIC and SD. Decision Making: Applications in Management and Engineering. 2021;4(2):76–105. https://doi.org/10.31181/dmame210402076i

12. Colakoglu F.N., Yazici A., Mishra A. Software Product Quality Metrics: A Systematic Mapping Study. IEEE Access. 2021;9:44647–44670. https://doi.org/10.1109/ACCESS.2021.3054730

13. Siebert J., Joeckel L., Heidrich J., et al. Construction of a quality model for machine learning systems. Software Quality Journal. 2022;30(2):307–335. https://doi.org/10.1007/s11219-021-09557-y

14. Lomio F., Moreschini S., Lenarduzzi V. A machine and deep learning analysis among SonarQube rules, product, and process metrics for fault prediction. Empirical Software Engineering. 2022;27(7):189. https://doi.org/10.1007/s10664-022-10164-z

15. Barletta V.S., Caivano D., Colizzi L., Dimauro G., Piattini M. Clinical-chatbot AHP evaluation based on "quality in use" of ISO/IEC 25010. International Journal of Medical Informatics. 2022;170(4):104951. https://doi.org/10.1016/j.ijmedinf.2022.104951

16. Vafaei N., Ribeiro R.A., Camarinha-Matos L.M. Assessing Normalization Techniques for Simple Additive Weighting Method. Procedia Computer Science. 2022;199:1229–1236. https://doi.org/10.1016/j.procs.2022.01.156

17. Arrow K.J., Chenery H.B., Minhas B.S., Solow R.M. Capital-Labor Substitution and Economic Efficiency. The Review of Economics and Statistics. 1961;43(3):225–250. https://doi.org/10.2307/1927286

18. Velepucha V., Flores P. A Survey on Microservices Architecture: Principles, Patterns and Migration Challenges. IEEE Access. 2023;11:88339–88358. https://doi.org/10.1109/ACCESS.2023.3305687

19. Jiménez-Fernández E., Sánchez Á., Ortega-Pérez M. Dealing with weighting scheme in composite indicators: An unsupervised distance-machine learning proposal for quantitative data. Socio-Economic Planning Sciences. 2022;83(2):101339. https://doi.org/10.1016/j.seps.2022.101339

20. Kizielewicz B., Shekhovtsov A., Sałabun W. pymcdm–The universal library for solving multi-criteria decision-making problems. SoftwareX. 2023;22:101368. https://doi.org/10.1016/j.softx.2023.101368

21. Liern V., Pérez-Gladish B. Building Composite Indicators With Unweighted-TOPSIS. IEEE Transactions on Engineering Management. 2023;70(5):1871–1880. https://doi.org/10.1109/TEM.2021.3090155

22. Krichen M. A Survey on Formal Verification and Validation Techniques for Internet of Things. Applied Sciences. 2023;13(14):8122. https://doi.org/10.3390/app13148122

23. Maclver D.R., Hatfield-Dodds Z., et al. Hypothesis: A new approach to property-based testing. The Journal of Open Source Software. 2019;4(43):1891. https://doi.org/10.21105/joss.01891

24. Papadakis M., Kintis M., Zhang J., Jia Y., Le Traon Y., Harman M. Mutation Testing Advances: An Analysis and Survey. Advances in Computers. 2019;112:275–378. https://doi.org/10.1016/bs.adcom.2018.03.015

25. Burkov V.N., Goubko M., Korgin N., Novikov D. Introduction to theory of control in organizations. Boca Raton: CRC Press; 2015. 346 p. https://doi.org/10.1201/b18152

26. Burkov V.N., Korgin N.A., Novikov D.A. Control Mechanisms for Organizational-Technical Systems: Problems of Integration and Decomposition. IFAC-PapersOnLine. 2016;49(32):1–6. https://doi.org/10.1016/j.ifacol.2016.12.180