Neural Categorizer - Detect Programming Framework-based Anomalies

Abstract

"Every day, a lot of social information, experience, and material are exchanged online. Since this sharing, the quantity of user-generated material is also growing quickly, ushering in the ""age of big data."" Developers often depend on programming frameworks for the effective and timely delivery of software. By maintaining a strong and happy user base, designers and developers work even harder to make them accessible to their customers. These programming frameworks, despite their best efforts, are plagued by a number of abnormalities, including documentation, ineffective memory and compatibility-based anomalies. Q&A platform had become one of the widespread targets for discussing this knowledge. However, the prevalence of unorganized postings is one amongst the primary problems with the questions and answers platforms that are now in use. This makes it challenging for the developers to find pertinent topics. The currently in use systems were designed with little consideration for learning state characteristics, such as natural language processing and tagged data for post classification. Nevertheless, given the unstructured nature of the data, NLP and labelled data alone would not be enough to detect problem postings. This research's findings have resulted in the creation of a gap. The study suggests an improved combination system that seeks to categorize and identify anomalies. The project's objective is to distinguish between regular posts and those linked to anomalies and to recommend these posts to users, designers, and developers in order to enhance maintainability on documentation, incorrect code names, out-of-date data, and a host of other difficulties. NLP would be used to gather the unstructured data and turn it into a structured format. In addition, a cluster neural method would be used to increase the accuracy of the anomaly identification. "