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Talent Scout
A
Ali Sayedsalehi
Concordia University
P
Peter C. Rigby
Concordia University
G
Gregory Mierzwinski
Mozilla
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Founder's Pitch
"Building a CI tool to save over $490K annually in infrastructure costs by optimizing performance regression testing with risk-aware batch strategies."
Commercial Viability Breakdown
0-10 scaleHigh Potential
2/4 signals
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4/4 signals
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4/4 signals
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Why It Matters
This research addresses the critical issue of performance regression in continuous integration, optimizing the process to reduce infrastructure costs and improve timely diagnostics, crucial for rapid software development cycles.
Product Angle
To productize, develop a SaaS solution that integrates with popular CI systems like Jenkins or GitHub Actions. Offer features like risk scoring, test batch optimization, and detailed analytics.
Disruption
This approach could replace traditional batch testing strategies that lack dynamic risk awareness, making software releases faster and more reliable by decreasing CI-associated regressions without increasing costs.
Product Opportunity
The market includes large software companies spending heavily on CI infrastructure, such as Mozilla, Google, and Facebook. They need cost-effective ways to manage testing resources, making them potential customers willing to pay for optimization tools.
Use Case Idea
A commercial tool for DevOps teams that implements risk-aware batch testing in CI workflows to cut costs and improve regression detection speed.
Science
The paper presents a method integrating machine-learned performance regression risk with adaptive batch testing in CI systems. It employs ML models like CodeBERT to predict the likelihood of performance regressions at the commit level, guiding the prioritization and grouping of commits for testing purposes.
Method & Eval
The approach was tested using a real-world Mozilla dataset, with risk models predicting commit-level regressions based on historical data. Results showed significant reduction in test executions and infrastructure costs.
Caveats
Risk predictions are only as good as the training data quality; poor implementation of unseen data might lead to inaccurate risk scores and potential delays in critical regression detection.
Author Intelligence
Ali Sayedsalehi
LEAD Concordia University
Peter C. Rigby
Concordia University
Gregory Mierzwinski
Mozilla