Improving Predictive Code Quality Using Machine Learning

Software Engineering has become an important aspect of driving product-led competitive advantage in the market for organizations. However, poor software quality reduces customer satisfaction. High-quality software, on the contrary, can prevent the need for repairs and reworks by more than 70%. This also reduces the associated application maintenance and support costs.

Over the years, we have witnessed significant advancements in Machine Learning (ML) and Deep Learning (DL) techniques, specifically in the domain of image, text, and speech processing. These advancements, coupled with readily available open-source code, its associated artifacts, and accelerated hardware, have facilitated the use of ML and DL techniques to address software engineering problems.

In this whitepaper, we address how this is done through quality matrices, which are dynamically collected and shape the developer model. How these techniques can be applied for software testing, source code quality analysis, program synthesis, code completion, and vulnerabilities analysis that involves source code analysis.

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