Optimal Web Service Selection Using UML Profile

Enterprises are more conscious of providing quality of services over the web for reasons of economy, reliability, interoperability and flexibility. Enterprise application relies on selection of the most appropriate service from several candidate services with similar capabilities provided by different service providers. The question is, on what basis the system chooses a service among several candidates. This paper proposes a model that makes an automatic selection of best service and detects the variance between the non-functional requirements of the users and service qualifications. In this paper, we describe our approach aimed to detect conflicts between user requirements and the service specifications of the service provider. Our work proposes to detect these conflicts using Ontology and UML profiles to achieve better performance and avoid unpredictable state of the system. We suggest use of UML extensions and domain Ontology to detect NFR conflicts between the client’s requirements and service specifications

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