Weakly Supervised Unit Test Quality Scoring via Generative Models
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Solution Overview
Problem
Inadequate code inspection can lead to significant disruptions, loss of customer confidence, and potential life-threatening issues due to poorly developed and poorly inspected code, as existing unit test methods fail to ensure robust application performance.
Innovation Solution
A system and method for weakly supervised unit test quality scoring using a generative model that parses code snippets via an abstract syntax tree, applies binary labeling functions to create a labelling matrix, trains a probabilistic generative model for pseudo-labels, and builds a discriminative model for accurate unit test scoring, providing actionable explanations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional unit test methods are used, then implementation is simple, but code inspection quality is inadequate
Solution Approach 1:
The patent introduces an intermediary machine learning model that acts as a mediator between the code snippets and the quality assessment process. This model transforms raw code into structured representations with quality scores, bridging the gap between simple traditional methods and complex manual inspection, thereby improving code inspection quality without requiring direct human involvement in every case.
Solution Approach 2:
The patent replaces manual code inspection mechanisms with an automated machine learning-based system. The mechanical process of human reviewers examining code is substituted with an electronic system that uses trained models to automatically assess code quality, maintaining or improving inspection quality while reducing system complexity in terms of human resource requirements.
2Measurement precision
If manual code inspection is performed, then code quality assessment is thorough, but time consumption increases
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models on large datasets of code before actual inspection. This preliminary training phase enables the models to quickly and accurately assess code quality during actual inspection, achieving thorough assessment accuracy without the time consumption of manual review for each code snippet.
Solution Approach 2:
The patent creates copies of code snippets and processes them through the machine learning model, which learns from multiple examples and variations. The model captures patterns from numerous code examples, enabling accurate quality assessment that replicates or exceeds manual inspection thoroughness while operating at automated speed, significantly reducing inspection time.
3Productivity
If automated testing is implemented, then productivity increases, but test quality may be insufficient
Solution Approach 1:
The patent implements feedback mechanisms where the machine learning model continuously learns from the results of unit tests and code quality assessments. The model receives feedback about which tests are high-quality and which are not, adjusting its assessments accordingly. This feedback loop enables automated testing to maintain high productivity while improving test quality over time through iterative learning.
Solution Approach 2:
The patent changes key parameters of the testing system by introducing learned quality scores and probabilistic assessments instead of simple pass/fail criteria. By adjusting the confidence thresholds and quality metrics dynamically based on learned patterns, the system maintains high productivity through automation while ensuring sufficient test quality through intelligent parameter adjustment.
Data Source
AI summary
Systems and methods for weakly supervised unit test quality scoring are disclosed. According to one embodiment, a method may include: parsing, by a generative model computer program, a plurality of code snippets in a repository using an abstract syntax tree; receiving, by the generative model computer program, a plurality of binary labelling functions from a labelling function repository; creating, by the generative model computer program, a labelling matrix by applying the binary labelling functions to the parsed code snippets; training, by the generative model computer program, a probabilistic generative model using the labelling matrix resulting in a vector of pseudo-labels; building, by a unit test scoring computer program, a discriminative model, wherein the discriminative model receives an array of real value inputs; and training, by the unit test scoring computer program, the discriminative model using the parsed code snippets, the vector of pseudo labels, and the abstract syntax tree.