PRESTO: A Framework for Predicting Software System Performance from SDLC Metrics (Design and Proof-of-Concept Evaluation)

Source abstract

DevOps toolchains record 164 metrics across eight software development lifecycle (SDLC) phases, yet no public dataset links these pre-deployment signals to production runtime outcomes. We present PRESTO, a framework for predicting software system performance from multi-phase SDLC metrics, plus a Gaussian copula-based synthetic data generator—both evaluated as a proof of concept, not a validated prediction method. Under nested cross-validation that selects the algorithm and hyperparameters within the training partition, non-autoregressive SDLC process features reach R2=0.108 to 0.620 across three synthetic domains; a target-correlation ablation shows that this is largely recovery of the generator’s specified structure, shared identically across all three domains, so the results show that the framework recovers relationships embedded in the generator. Across four real-world datasets, the primary validation analyses are Mozilla Perfherder’s autoregressive persistence on a real production-performance signal (R2=0.17 to 0.92) and SQuaD’s CVE-count (R2=0.23) and static-analysis-enriched defect-fix (R2=0.50) results; the confidence intervals are wide throughout, and most remaining comparisons include zero. The central limitation is that no available dataset joins eight-phase SDLC telemetry to production uptime, so each real dataset tests only a narrower proxy outcome (build duration, browser-performance regression, CI workflow duration, defect and CVE counts). PRESTO is therefore a framework and benchmark whose central proposition remains untested.