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Systematic literature review of IoT metrics

Moulla Donatien Koulla Moulla, Mnkandla Ernest, Abran Alain

Applied Computer Science

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2023

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Vol. 19, no 1

64--81

EN

The Internet of Things (IoT) touches almost every aspect of modern society and has changed the way people live, work, travel and, do business. Because of its importance, it is essential to ensure that an IoT system is performing well, as desired and expected, and that this can be assessed and managed with an adequate set of IoT performance metrics. The aim of this study is to systematically inventory and classify recent studies that have investigated IoT metrics. The authors conducted a literature review based on studies published between January 2010 and December 2021 using a set of five research questions (RQs) on the current knowledge bases for IoT metrics. A total of 158 IoT metrics were identified and classified into 12 categories according to the different parts and aspects of an IoT system. To cover the overall performance of an IoT system, the 12 categories were organized into an ontology. The results show that the category of network metrics was most frequently discussed in 43% of the studies and, with the highest number of metrics at 37%. This study can provide guidelines for researchers and practitioners in selecting metrics for IoT systems and valuable insights into areas for improvement and optimization.

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System performance requirements: A standards-based model for early identification, allocation to software functions and size measurement

Al-Sarayreh Khalid T., Meridji Kenza, Abran Alain, Trudel Sylvie

e-Informatica Software Engineering Journal

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2020

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Vol. 14, nr 1

117--148

EN

Background: In practice, the developers focus is on early identification of the functional requirements (FR) allocated to software, while the system non-functional requirements (NFRs) are left to be specified and detailed much later in the development lifecycle. Aim: A standards-based model of system performance NFRs for early identification and measurement of FR-related performance of software functions. Method: 1) Analysis of performance NFR in IEEE and ECSS standards and the modeling of the identified system/software performance functions using Softgoal Interdependency Graphs. 2) Application of the COSMIC-FSM method (e.g., ISO 19761) to measure the functional size of the performance requirements allocated to software functions. 3) Use of the COSMIC-SOA guideline to tailor this framework to service-oriented architecture (SOA) for performance requirements specification and measurement. 4) Illustration of the applicability of the proposed approach for specification and measurement of system performance NFR allocated to the software for an automated teller machine (ATM) in an SOA context. Results: A standards-based framework for identifying, specifying and measuring NFR system performance of software functions. Conclusion: Such a standards-based system performance reference framework at the function and service levels can be used early in the lifecycle by software developers to identify, specify and measure performance NFR.

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Empirical Studies on Software Product Maintainability Prediction: A Systematic Mapping and Review

Elmidaoui Sara, Cheikhi Laila, Idri Ali, Abran Alain

e-Informatica Software Engineering Journal

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2019

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Vol. 13, nr 1

141--202

EN

Background: Software product maintainability prediction (SPMP) is an important task to control software maintenance activity, and many SPMP techniques for improving software maintainability have been proposed. In this study, we performed a systematic mapping and review on SPMP studies to analyze and summarize the empirical evidence on the prediction accuracy of SPMP techniques in current research. Objective: The objective of this study is twofold: (1) to classify SPMP studies reported in the literature using the following criteria: publication year, publication source, research type, empirical approach, software application type, datasets, independent variables used as predictors, dependent variables (e.g. how maintainability is expressed in terms of the variable to be predicted), tools used to gather the predictors, the successful predictors and SPMP techniques, (2) to analyze these studies from three perspectives: prediction accuracy, techniques reported to be superior in comparative studies and accuracy comparison of these techniques. Methodology: We performed a systematic mapping and review of the SPMP empirical studies published from 2000 up to 2018 based on an automated search of nine electronic databases. Results: We identified 82 primary studies and classified them according to the above criteria. The mapping study revealed that most studies were solution proposals using a history-based empirical evaluation approach, the datasets most used were historical using object-oriented software applications, maintainability in terms of the independent variable to be predicted was most frequently expressed in terms of the number of changes made to the source code, maintainability predictors most used were those provided by Chidamber and Kemerer (C&K), Li and Henry (L&H) and source code size measures, while the most used techniques were ML techniques, in particular artificial neural networks. Detailed analysis revealed that fuzzy & neuro fuzzy (FNF), artificial neural network (ANN) showed good prediction for the change topic, while multilayer perceptron (MLP), support vector machine (SVM), and group method of data handling (GMDH) techniques presented greater accuracy prediction in comparative studies. Based on our findings SPMP is still limited. Developing more accurate techniques may facilitate their use in industry and well-formed, generalizable results be obtained. We also provide guidelines for improving the maintainability of software.