In software documentation, product knowledge and software requirement are very important to improve product quality. Reading of whole documentation of large corpus cannot be possible by developers in maintenance stage. They need to receive software documentation entities i.e. (development, designing and testing etc.) in a short period of time. In software documentation an important documents are able to record. There exists a space between information which developer wants and software documentation. This difference can be experimental whenever developers effort to discover the accurate information in the correct form at the exact time. To solve this problem, an approach for extracting relevant task of the documentation under four phases of software entities (i.e. documentation, development, testing and other etc.) is described. The main idea is task extracted from the software documentation, freeing the developer easily get the required data from software documentation with customize portal using Natural Language Processing (NLP) and then the category of task can be generated easily from existing applications. The machine learning approach that is based on supervised learning technique for training dataset in the form of text files based on text mining. Our approach use WordNet library to identify relevant tasks for calculating frequency of each word which allows developers in a piece of software to discover the word usage and also assigning Part-of Speech (POS) to each word. The result shows that task is extracted by calculating how many sentences, tokens and tasks appearing in a document and also shows task is relevant or not. It also reduced a live space between information which developers want and software documentation. This is used to improve the performance of system by taking feedback of developers. The result is identified through customize portal which helps to developers easily get information in a short period of time. The system is 80% precise to extract task by taking feedback of developers in the form of comment.

Natural language processing, text mining, part-of-speech tagging, text files, machine learning techniques, WordNet library

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