Semi-automated Software Requirements Categorisation using Machine Learning Algorithms

Requirement engineering is a mandatory phase of the Software development life cycle (SDLC) that includes defining and documenting system requirements in the Software Requirements Specification (SRS). As the complexity increases, it becomes difficult to categorise the requirements into functional and non-functional requirements. Presently, the dearth of automated techniques necessitates reliance on labour-intensive and time-consuming manual methods for this purpose. This research endeavours to address this gap by investigating and contrasting two prominent feature extraction techniques and their efficacy in automating the classification of requirements. Natural language processing methods are used in the text pre-processing phase, followed by the Term Frequency – Inverse Document Frequency (TF-IDF) and Word2Vec for feature extraction for further understanding. These features are used as input to the Machine Learning algorithms. This study compares existing machine learning algorithms and discusses their correctness in categorising the software requirements. In our study, we have assessed the algorithms Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Neural Network (NN), K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) on the precision and accuracy parameters. The results obtained in this study showed that the TF-IDF feature selection algorithm performed better in categorising requirements than the Word2Vec algorithm, with an accuracy of 91.20% for the Support Vector Machine (SVM) and Random Forest algorithm as compared to 87.36% for the SVM algorithm. A 3.84% difference is seen between the two when applied to the publicly available PURE dataset. We believe these results will aid developers in building products that aid in requirement engineering

Big Data and Social Media Analytics: A Key to Understanding Human Nature

Big Data and Social Media have transformed knowledge and comprehension in this age of technological advancement. Corporate leaders and professionals in several industries have focused on Big Data, a large collection of data from multiple sources. Meanwhile, social media networks' fast data growth has been lauded as a way to comprehend human behaviours. This study paper examines the critical need to extract intelligent information from the large volume, wide variety, and quick pace of data to meet modern corporate needs. Using specialized tools and procedures for large-scale dataset analysis and effective data management structures are crucial in this context. Big Data and Social Media Analytics offer new insights into human behaviour. This study analyzes how these two fields may work together to create new management strategies. We show that Big Data and Social Media Analytics may provide unmatched opportunities for understanding human behaviour through practical examples and case studies. This integration helps organizations navigate a rapidly changing global market by assessing client preferences, anticipating industry trends, and understanding societal shifts. This study emphasizes the need of using modern technical driving elements to better understand human nature. Integration of several data sources provides insights that give a competitive edge and aid decision-making across sectors. This article examines Big Data and Social Media Analytics, which improves management tactics and deepens understanding of the complex network of human activities and attitudes

Aleukemic granulocytic sarcoma and leukemia cutis: A report of two rare cases and review of literature

Granulocytic sarcoma (GS), also called myeloid sarcoma is an extramedullary tumor of the immature granulocytic cells. It is a rare entity and mostly accompanied by acute myeloid leukemia (AML). Very rarely, it is detected before clinical signs of leukemia or other diseases. When the bone marrow biopsy reveals no other hematologic malignancies, the GS is described as aleukemic, primary or isolated. Here, we report two rare cases, one of which presented as aleukemic GS of lymph nodes with aleukemic leukemia cutis, and the other with aleukemic GS of lung. Both cases posed diagnostic dilemma in view of their atypical presentations and site of involvement. Final diagnosis was made by immunohistochemistry (IHC). Both patients were treated with standard induction chemotherapy for AML. One patient had relapsed on treatment and was further treated with only 6-thioguanine leading to complete remission. Our cases emphasize the importance of early suspicion and use of IHC in diagnosis of aleukemic GS and also potential role of oral thioguanine alone in relapsed cases not eligible for hematopoietic stem cell transplant