An Automatic Generation and Verification Method of Software Requirements Specification

Use case diagramming and class diagramming are important requirements engineering techniques that play an essential role in modeling software specifications and facilitating the software development process. Software requirements are often expressed in natural language, which can be ambiguous, noisy, unmeasurable, and open to interpretation. This research overcomes this problem by extracting the required elements to generate use cases and class diagrams automatically from software requirements documents written in a common natural language. Therefore, an automated framework is proposed based on natural language processing techniques, such as tokenization and part-of-speed tagging, to parse the software requirements syntactically using a set of heuristic rules to facilitate the extracting actors, use cases, entities, relationships, and attributes used in the background to generate the use cases and class diagrams. Furthermore, to improve the performance of the proposed framework, the k-nearest neighbor algorithm is employed by predicting the pre-processed requirements. The proposed framework is evaluated using two performance measurements: recall and precision. The experiment results show that the proposed framework can achieve an average recall of 96% and an average precision of 92%.

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Self-Adaptable Software for Pre-Programmed Internet Tasks: Enhancing Reliability and Efficiency

Martínez García,

Martínez Rodríguez,

Pérez Zúñiga

2024

Applied Sciences

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In the current digital landscape, artificial intelligence-driven automation has revolutionized efficiency in various areas, enabling significant time and resource savings. However, the reliability and efficiency of software systems remain crucial challenges. To address this issue, a generation of self-adaptive software has emerged with the ability to rectify errors and autonomously optimize performance. This study focuses on the development of self-adaptive software designed for pre-programmed tasks on the Internet. The software stands out for its self-adaptation, automation, fault tolerance, efficiency, and robustness. Various technologies such as Python, MySQL, Firebase, and others were employed to enhance the adaptability of the software. The results demonstrate the effectiveness of the software, with a continuously growing self-adaptation rate and improvements in response times. Probability models were applied to analyze the software’s effectiveness in fault situations. The implementation of virtual cables and multiprocessing significantly improved performance, achieving higher execution speed and scalability. In summary, this study presents self-adaptive software that rectifies errors, optimizes performance, and maintains functionality in the presence of faults, contributing to efficiency in Internet task automation.

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An Automatic Generation and Verification Method of Software Requirements Specification

Cited by 3 publications

References 17 publications

Using LLMs in Software Requirements Specifications: An Empirical Evaluation

Using LLMs in Software Requirements Specifications: An Empirical Evaluation

An Intelligent Framework to Generate Use Case Diagrams and Class Diagrams from Requirements Documents

Self-Adaptable Software for Pre-Programmed Internet Tasks: Enhancing Reliability and Efficiency

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