Revolutionizing Software Project Development: A CNN-LSTM Hybrid Model for Effective Defect Prediction

Within the domain of software development, the practice of software defect prediction (SDP) holds a central and critical position, significantly contributing to the efficiency and ultimate success of projects. It embodies a proactive approach that harnesses data-driven techniques and analytics to preemptively identify potential defects or vulnerabilities within software systems, thereby enhancing overall quality and reliability while significantly impacting project timelines and resource allocation. The efficiency of software development projects hinges on their ability to adhere to deadlines, budget constraints, and deliver high-quality products. SDP contributes to these objectives through various means. This paper introduces a novel SDP model that harnesses the combined capabilities of Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTMs) unit. CNNs excel at extracting features from structured data, enabling them to discern patterns and dependencies within code repositories and change histories. LSTMs, conversely, excel in handling sequential data, which is pivotal for capturing the temporal aspects of software development and tracking the evolution of defects over time. The outcomes of the proposed CNN-LSTM hybrid model showcase its superior predictive performance. Simulation results affirm the substantial potential of this model to bolster the efficiency and reliability of software development processes. As technology advances and data-driven methodologies become increasingly prevalent in the software industry, the integration of such hybrid models presents a promising avenue for continually elevating software quality and ensuring the triumph of software projects. In summary, the utilization of this innovative SDP model offers a transformative approach to efficient software development, positioning it as a vital tool for project success and quality assurance.