Mechanisms for Bug Management based on Artificial Intelligence

Artificial Intelligence

Defects are essentially the basic property of the basic properties of the system; they can occur at any stage of the product life cycle, from design and specifications to production and development. The cost of resolving the issue or a bug is contingent on which stage the bug is found in; the later the bug is detected in the system, the higher the cost is likely to be.

The presence of bugs in the software application has a substantially detrimental effect on the reliability, overall quality, and maintenance and fixing costs. More than 80% of the development budget of the software development is allocated to quality control and quality assurance; the cost for the global economy of detecting and fixing the software bugs is estimated to be around $300 billion. 

Even with the most experienced quality assurance managers and teams, there is a probability that some bugs might be overlooked and the defect escape rate will be high. Hence, in agile-driven organizations today where one of the main goals of companies is to reduce the time to market without compromising on quality, it is important for managers to embed software bug prediction models from the initial phases.

By combining bug management tools with the sophisticated techniques and methodologies of artificial intelligence, the enterprise is able to user satisfaction by improving the overall software performance. In addition to that, predicting the defect-prone modules of the software and detecting bugs early in the system enables the team to achieve better resource utilization, and allows them to enhance the ability of the software to adapt to different environments.

How Artificial Intelligence is used for Software Prediction?

Artificial Intelligence is an umbrella term that covers numerous techniques and statistical models like Machine learning which are used extensively in the software bug prediction models. Based on historical information retrieved from the recently completed projects, machine learning is widely used to tackle bug prediction problems in software applications. Two of the main components of the historical information which is required for the prediction models include essential software metrics and multiple computing techniques to analyze the said data.

The deep defect and vulnerability prediction software which is based on artificial intelligence mechanisms empowers the development teams to create software products in compliance with the quality standards on time and at a reduced cost. This is why the past few years have seen a dramatic increase in demand for the automation of bug management tools and the use of artificial intelligence and machine learning to enhance the quality and dependability of the software. 

Machine learning and AI technology make use of deep learning methods to create self-improving and accurate prediction models that can spot any defect-prone or susceptible code regions automatically. It essentially analyzes the suspicious code file and commits to highlighting any problems or weak nodes. Moreover, the prediction models also reduce the invested time and testing efforts of the team and allow them to focus on more important issues as it offers actionable insights and feedback.

The feedback generally includes detailed and comprehensive information related to the bug, including the exact location of the bug, adequate details about the prediction of the bugs, and enough information for the developers to fix the issues.  

Machine Learning Algorithms for Software Bug Prediction Models

There are three main supervised machine learning algorithms that are used to establish an inferring function by concluding dependencies and relations between the given inputs and outputs of the training dataset. Based on the inferring function derived previously, new predictions about the output value of new input data can be made. These supervised machine learning algorithms include;

Naïve Bayes – based on Bayes theorem which states that the existence or lack of any feature belonging to any class is not contingent on the absence or presence of any other feature. It’s a simple yet effective probabilistic classifier grounded in the common principle that assumes the independence of features.

Artificial Neural Networks – inspired by the biological neural networks, the artificial neural networks have the ability to classify non-linear variables and to model complex relationships between multiple features. The neurons in this network work together concurrently towards a common output.

Decision Trees – a hierarchical and predictive model which uses the observation of an item as a branch to reach the target value symbolized by the leaf. Primarily used for data mining, this is a common learning method that has more than one branch and leaf node, which are representative of the decisions. 


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