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Altran Improves Software Quality With Machine Learning

Altran, the worldwide leader in engineering and R&D services, announced the release of a new tool on r that predicts the likelihood of bugs in source code created by developers early in the program development process. By making use of machine learning (ML) to historical data, the tool – called “Code Defect AI” – identifies areas of the code that are potentially buggy and then suggests some tests to diagnose and connect the flaws, leading to higher-quality software and faster development times.

Bugs are a fact of life in software development. The later a defect can be found in the expansion lifecycle, the higher the price of fixing a bug. This bug-deployment-analysis-fix process is time consuming and costly. Code Defect AI allows earlier discovery of defects, minimizing the price of fixing them and speeding the development cycle.

“It's well-known that software developers are under constant pressure to produce code fast without compromising on quality,” said Walid Negm, Group Chief Innovation Officer at Altran. “The reality however would be that the software release cycle needs more than automation of assembly and delivery activities. It requires algorithms that can help make strategic judgments – especially as code gets more complex. Code Defect AI does just that.”

Code Defect AI relies on various ML techniques including random decision forests, support vector machines, multilayer perceptron (MLP) and logistic regression. Historical information is extracted, pre-processed and labelled to train the algorithm and curate a dependable decision model. Developers receive a confidence score that predicts if the code is compliant or presents the risk of containing bugs.

Code Defect AI supports integration with third-party analysis tools and can itself help identify bugs in a given program code. Additionally, the Code Defect AI tool allows developers to assess featuring within the code have higher weightage in terms of bug prediction, i.e., should there be two features within the software that lead to the assessment of the probable bug, which feature will take precedence.

“Microsoft and Altran have been working together to improve the software development cycle, and Code Defect AI, powered by Microsoft Azure, is an innovative tool that will help software developers through the use of machine learning,” said David Carmona, General Manager of AI Marketing at Microsoft.

Code Defect AI is a scalable solution that can be hosted on premise as well as on cloud computing platforms for example Microsoft Azure. While the solution currently supports GitHub, which is owned by Microsoft, it may be integrated with other source-code management tools as needed.

The tool can also be available on the Microsoft AI Lab portal to ensure that Microsoft developers can download the answer and employ it internally.