Predictive Metrics: Transforming Engineering Productivity and Software Quality |
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| © 2025 by IJCTT Journal | ||
| Volume-73 Issue-1 |
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| Year of Publication : 2025 | ||
| Authors : Saumen Biswas | ||
| DOI : 10.14445/22312803/IJCTT-V73I1P106 | ||
How to Cite?
Saumen Biswas, "Predictive Metrics: Transforming Engineering Productivity and Software Quality," International Journal of Computer Trends and Technology, vol. 73, no. 1, pp. 51-56, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I1P106
Abstract
The prospect of using data-driven metrics to improve software quality and engineering productivity in the constantly evolving software engineering landscape is vast. This paper explores and demonstrates the creation and implementation of critical metrics that improve organizational outcomes. The research presents an innovative framework for designing and implementing key performance indicators, integrating pull request workflow analysis, release monitoring, real-time alerting and automated reporting. State-of-the-art techniques for predictive analysis are studied and implemented, demonstrating how metrics can promote continuous improvement within software teams. The study demonstrates how institutions can accomplish faster time-to-market, improved operational efficiency, and greater customer satisfaction by associating these metrics with business outcomes. This work contributes to the field by providing a methodology for leveraging predictive metrics to transition from reactive to proactive decision-making, improving software engineering practices.
Keywords
Engineering productivity, Machine learning, Metrics, Operational efficiency, Predictive analytics, Software quality.
Reference
[1] Jez Humble, and David Farley, Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation, Pearson Education, pp. 1-512, 2010.
[Google Scholar] [Publisher Link]
[2] Steve McConnell, Code Complete: A Practical Handbook of Software Construction, Microsoft Press, pp. 1-914, 2004.
[Google Scholar] [Publisher Link]
[3] Gene Kim, Kevin Behr, and George Spafford, The Phoenix Project: A Novel about IT, DevOps, and Helping Your Business Win, IT Revolution Press, pp. 1-343, 2013.
[Google Scholar] [Publisher Link]
[4] Nicole Forsgren et al., Accelerate The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations, IT Revolution Press, pp. 1-288, 2018.
[Google Scholar] [Publisher Link]
[5] Martin Fowler, and Matthew Foemmel, Continuous Integration, 2000.
[Google Scholar] [Publisher Link]
[6] IEEE Standards, 730-2014 - IEEE Standard for Software Quality Assurance Processes, IEEE, pp. 1-138, 2014.
[Publisher Link]
[7] Torgeir Dingsøyr, and Casper Lassenius, “Emerging Themes in Agile Software Development: Introduction to the Special Section on Continuous Value Delivery,” Information and Software Technology, vol. 77, pp. 56-70, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Pilar Rodríguez et al., “Continuous Deployment of Software-Intensive Products and Services: A Systematic Mapping Study,” Journal of Systems and Software, vol. 123, pp. 263-291, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Marko Leppänen et al., “The Highways and Country Roads to Continuous Deployment,” IEEE Software, vol. 32, no. 2, pp. 64-72, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Tim Menzies, and Thomas Zimmermann, “Software Analytics: So What?,” IEEE Software, vol. 30, no. 4, pp. 31-37, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Eero Laukkanen et al., “Bottom-up Adoption of Continuous Delivery in a Stage-gate Managed Software Organization,” Proceedings of the 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, Ciudad Real Spain, pp. 1-10, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Lianping Chen, “Continuous Delivery: Huge Benefits, but Challenges Too,” IEEE Software, vol. 32, no. 2, pp. 50-54, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Yue Jia, and Mark Harman, “An Analysis and Survey of the Development of Mutation Testing,” IEEE Transactions on Software Engineering, vol. 37, no. 5, pp. 649-678, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Nicole Forsgren, and Jez Humble, “The Role of Continuous Delivery in IT and Organizational Performance,” Proceedings of the Western Decision Sciences Institute, Las Vegas, NV, pp. 1-15, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Georgios Gousios, Martin Pinzger, and Arie Van Deursen, “An Exploratory Study of the Pull-Based Software Development Model,” Proceedings of the 36th International Conference on Software Engineering, Hyderabad India, pp. 345–355, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Kurt Matzler and Hans H. Hinterhuber, “How to Make Product Development Projects more Successful by Integrating Kano's Model of Customer Satisfaction into Quality Function Deployment,” Technovation, vol. 18, no. 1, pp. 25-38, 1998.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Andrew Meneely, Ben Smith, and Laurie Williams, “Validating Software Metrics: A Spectrum of Philosophies,” ACM Transactions on Software Engineering and Methodology vol. 21, no. 4, pp. 1-28, 2013.
[CrossRef] [Google Scholar] [Publisher Link]