iTaskDev: A Software as a Service with Adaptive Algorithm for Optimized Software Development Planning Across Methodologies

In software development, selecting the appropriate Software Development Life Cycle (SDLC) can be challenging due to the wide variety of available models, each suited to different project needs. Existing software tools often fail to capitalize on SDLC frameworks and lack automation capabilities. This study proposes a web-based workspace system designed to streamline software engineering projects. The system leverages a Natural Language Processing (NLP) model to recommend suitable SDLCs, optimizing the development process through a dynamic programming algorithm that automates task allocation, adapting to the recommended SDLC approach. The NLP model was developed using Azure ML with the OpenAI API to ensure the accuracy of SDLC suggestions. Automated task allocation and real-time notification were tested across various project scenarios. GitHub API services were implemented to provide project analytics through repository commits and issue tracking. After the system's development, the study evaluated user perceptions and satisfaction regarding iTaskDev's main objectives through Likert scale method. The NLP-based methodology suggested, and workspace system achieved a mean score of 3.75, whereas task automation along with project analytics through dynamic programming algorithms and API integration attained mean scores of 3.63 and 3.68, respectively. The system was further evaluated using ISO/IEC 25010 standards, demonstrating strong performance in functional suitability, performance efficiency, usability, and reliability, with an overall mean score of 3.67 out of 4.