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Mining Non-Functional Requirements using Machine Learning Techniques

Jindal Rajni, Malhotra Ruchika, Jain Abha, Bansal Ankita

e-Informatica Software Engineering Journal

2021

Vol. 15, nr 1

85--114

Background: Non-Functional Requirements (NFR) have a direct impact on the architecture of the system, thus it is essential to identify NFRs in the initial phases of software development. Aim: The work is based on extraction of relevant keywords from NFR descriptions by employing text mining steps and thereafter classifying these descriptions into one of the nine types of NFRs. Method: For each NFR type, keywords are extracted from a set of pre-categorized specifications using Information-Gain measure. Then models using 8 Machine Learning (ML) techniques are developed for classification of NFR descriptions. A set of 15 projects (containing 326 NFR descriptions) developed by MS students at DePaul University are used to evaluate the models. Results: The study analyzes the performance of ML models in terms of classification and misclassification rate to determine the best model for predicting each type NFR descriptions. The Naïve Bayes model has performed best in predicting “maintainability” and “availability” type of NFRs. Conclusion: The NFR descriptions should be analyzed and mapped into their corresponding NFR types during the initial phases. The authors conducted cost benefit analysis to appreciate the advantage of using the proposed models.

SIoT Framework: Towards an Approach for Early Identification of Security Requirements for Internet-of-things Applications

Jabangwe Ronald, Nguyen-Duc Anh

e-Informatica Software Engineering Journal

2020

Vol. 14, nr 1

77--95

Background: Security has become more of a concern with the wide deployment of Internet-of-things (IoT) devices. The importance of addressing security risks early in the development lifecycle before pushing to market cannot be over emphasized. Aim: To this end, we propose a conceptual framework to help with identifying security concerns early in the product development lifecycle for Internet-of-things, that we refer to as SIoT (Security for Internet-of-Things). Method: The framework adopts well known security engineering approaches and best practices, and systematically builds on existing research work on IoT architecture. Results: Practitioners at a Norwegian start-up company evaluated the framework and found it useful as a foundation for addressing critical security concerns for IoT applications early in the development lifecycle. The output from using the framework can be a checklist that can be used as input during security requirements engineering activities for IoT applications. Conclusions: However, security is a multi-faced concept; therefore, users of the SIoT framework should not view the framework as a panacea to all security threats. The framework may need to be refined in the future, particularly to improve its completeness to cover various IoT contexts.