Summary of ALTRAN IMPROVES SOFTWARE QUALITY WITH MACHINE LEARNING
Code Defect AI is a new machine learning tool by Altran that predicts software bugs early in the development cycle to reduce fix costs and accelerate delivery. By analyzing historical data, it identifies risky code areas and suggests tests for diagnosis. The system uses algorithms like random decision forests and logistic regression to provide confidence scores, helping developers prioritize fixes and improve overall software quality before deployment.
Parts used in Code Defect AI:
- Random decision forests
- Support vector machines
- Multilayer perceptron (MLP)
- Logistic regression
- Historical data extraction module
- Data pre-processing component
- Labeling system
- Decision model curator
- Confidence score generator
- Bug weightage assessment feature
New tool, ‘Code Defect AI,’ allows earlier discovery of bugs, minimizing the cost to fix them and speeding up the development cycle.
Altran, the global leader in engineering and R&D services, today announced the release of a new tool available on GitHub that predicts the likelihood of bugs in source code created by developers early in the software development process. By applying machine learning (ML) to historical data, the tool – called “Code Defect AI” – identifies areas of the code that are potentially buggy and then suggests a set of tests to diagnose and fix the flaws, resulting in higher-quality software and faster development times.
Bugs are a fact of life in software development. The later a defect is found in the development lifecycle, the higher the cost 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 cost of fixing them and speeding the development cycle.
It’s well known that software developers are under constant pressure to release code fast without compromising on quality,” said Walid Negm, Group Chief Innovation Officer at Altran. “The reality however is that the software release cycle needs more than automation of assembly and delivery activities. It needs algorithms that can help make strategic judgments ‒ especially as code gets more complex. Code Defect AI does exactly that.
Code Defect AI relies on various ML techniques including random decision forests, support vector machines, multilayer perceptron (MLP) and logistic regression. Historical data is extracted, pre-processed and labelled to train the algorithm and curate a reliable decision model. Developers are given a confidence score that predicts whether 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 which features in the code have higher weightage in terms of bug prediction, i.e., if there are two features in the software that play a role in the assessment of a probable bug, which feature will take precedence.
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- What does Code Defect AI do?
It predicts the likelihood of bugs in source code early in the software development process. - How does the tool minimize bug fixing costs?
It allows earlier discovery of defects before they reach the deployment phase. - Which machine learning techniques does Code Defect AI rely on?
It uses random decision forests, support vector machines, multilayer perceptron, and logistic regression. - Can developers see how confident the tool is about a bug?
Yes, developers are given a confidence score predicting if code is compliant or risky. - Does Code Defect AI integrate with other tools?
Yes, it supports integration with third-party analysis tools. - How does the tool help prioritize which code features to fix?
It assesses which features have higher weightage in terms of bug prediction. - What is the main benefit of using this tool over manual testing?
It speeds up the development cycle by identifying flaws earlier than traditional methods.
