Implicit Requirements (IMR) identification is part of the Requirements Engineering (RE) phase in Software Engineering during which data is gathered to create SRS (Software Requirements Specifications) documents. As opposed to explicit requirements clearly stated, IMRs constitute subtle data and need to be inferred. Research has shown that IMRs are crucial to the success of software development. Many software systems can encounter failures due to lack of IMR data management. SRS documents are large, often hundreds of pages, due to which manually identifying IMRs by human software engineers is not feasible. Moreover, such data is evergrowing due to the expansion of software systems. It is thus important to address the crucial issue of IMR data management. This article presents a survey on IMRs in SRS documents with the definition and overview of IMR data, detailed taxonomy of IMRs with explanation and examples, practices in managing IMR data, and tools for IMR identification. In addition to reviewing classical and state-of-the-art approaches, we highlight trends and challenges and point out open issues for future research. This survey article is interesting based on data quality, hidden information retrieval, veracity and salience, and knowledge discovery from large textual documents with complex heterogeneous data.

Mobile app developers are always looking for ways to use the reviews (provided by their app's users) to improve their application (e.g., adding a new functionality in the app that a user mentioned in their review). Usually, there are thousands of user reviews that are available for each mobile app and isolating software requirements manually from such as big dataset can be difficult and time-consuming. The primary objective of the current research is to automate the process of extracting functional requirements and filtering out non-requirements from user app reviews to help app developers better meet the wants and needs of their users. This paper proposes and evaluates machine learning based models to identify and classify software requirements from both, formal Software Requirements Specifications (SRS) documents and Mobile App Reviews (written by users) using machine learning (ML) algorithms combined with natural language processing (NLP) techniques. Initial evaluation of our ML-based models show that they can help classify user app reviews and software requirements as Functional Requirements (FR), Non-Functional Requirements (NFR), or Non-Requirements (NR).

This paper proposes a pilot approach based on the comparative analysis of supervised Machine Learning models coupled with basic Natural Language Processing concepts for classifying Functional and Non-Functional Requirements from huge collections of data relevant to the Requirements Engineering (RE) phase within software development. The publicly available PROMISE Software Engineering Repository dataset is used in the execution of this approach. Non-Functional Requirements are further classified into subclasses based on attributes they address since they are not directly related to the core functions of the concerned software. This overall research initiative helps to make the RE phase more efficient and reduces human effort in software development. It leverages Big Data in Software Engineering.

Machine learning (ML) is becoming a powerful tool for a variety of applications where artificial intelligence solutions are required. A ML benchmark is a standard suite to measure, evaluate and compare the performance and efficiency of ML systems. This study analyzes the benchmark results from two famous benchmarks MLMark and MLPerf to provide a basis of comparison between both benchmarks as well as to provide recommendations on computer architectures to utilize for ML inferencing. Lastly, special emphasis is placed on the performance of edge AI devices.