Abstract
Abstract: Software Flaw Projection (SFP) is an important issue in software development and maintenance process. Software flaws can cause significant problems for software development teams. So, projecting the software faults in earlier phase improves the software quality, reliability, efficiency and reduces the software cost. However, developing robust flaw projection model is a challenging task and many techniques have been proposed. Projecting the likelihood of flaws occurring in software can help developers prevent or mitigate their impact. This paper presents a software flaw projection model based on Machine Learning (ML) algorithms. Supervised ML algorithms have been used to predict future software faults based on historical data. The evaluation process proved that ML algorithms can be used effectively with high accuracy rate. Furthermore, a comparison measure is applied to compare the proposed prediction model with other approaches. The collected results showed that the ML approach has a better performance
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