Efficiency is crucial in today’s fast-paced business world, and Agile software development has become a widely adopted approach to streamline work processes. However, translating user stories or epics into pseudocode can be a time-consuming task, consuming a significant portion of an industrial project. To address this challenge, we propose a methodology that leverages Natural Language Processing (NLP) techniques to simplify the development process in Agile organizations. Our approach divides the text-to-pseudocode conversion task into two stages: input text processing and encoder architecture. The input text processing stage involves breaking down the given natural language description into individual words or symbols, called tokenization, and identifying their part of speech, called POS tagging. The encoder stage utilizes a transformer-based architecture to process the preprocessed text and generate pseudocode. By simplifying the development process through NLP-based code generation, we aim to improve overall efficiency in Agile software development projects.
I. INTRODUCTION
In Agile software development, efficiency is crucial for successful project delivery. To facilitate this, developers need to understand user requirements and translate them into code. However, the process of translating natural language descriptions into pseudocode can be a significant bottleneck, consuming a substantial portion of an industrial project. To address this challenge, we propose a methodology that leverages NLP techniques to streamline the development process in Agile organizations.
II. OUR METHODOLOGY
Our methodology consists of two stages: input text processing and encoder architecture.
A. Input Text Processing
The first stage involves breaking down the given natural language description into individual words or symbols, called tokenization, and identifying their part of speech, called POS tagging. Tokenization is the process of dividing text into meaningful units, or tokens, while POS tagging is the process of labeling each token with its corresponding part of speech, such as noun, verb, adjective, etc.
B. Encoder Architecture
The second stage utilizes a transformer-based architecture to process the preprocessed text and generate pseudocode. The encoder takes in the preprocessed text and processes it using a transformer-based architecture. This allows for efficient and accurate code generation, ensuring that the resulting pseudocode is easy to understand and implement.
III. BENEFITS OF OUR METHODOLOGY
Our methodology simplifies the development process in Agile software development projects by leveraging NLP techniques for text-to-pseudocode conversion. This approach has several benefits, including:
A. Reduced Time and Effort
By automating the text-to-pseudocode conversion process, our methodology reduces the time and effort required to translate user stories or epics into code. This allows developers to focus on other critical tasks, such as coding and testing, leading to a more efficient development process.
B. Improved Accuracy
Our methodology ensures accurate code generation by leveraging NLP techniques, which are well-established in the field of natural language processing. This accuracy reduces the likelihood of errors in the generated pseudocode, making it easier to implement and maintain.
C. Enhanced Readability
The resulting pseudocode is easy to understand and read, making it simpler for developers to work with. This enhances collaboration and communication within development teams, leading to better project outcomes.
IV. CONCLUSION
In conclusion, our methodology simplifies the development process in Agile software development projects by leveraging NLP techniques for text-to-pseudocode conversion. By automating this process, we reduce time and effort, improve accuracy, and enhance readability. With the increasing adoption of Agile software development, our methodology has the potential to significantly improve overall efficiency in industrial projects.
