Software module clustering: An in-depth literature analysis

Software module clustering is an unsupervised learning method used to cluster software entities (e.g., classes, modules, or files) with similar features. The obtained clusters may be used to study, analyze, and understand the software entities' structure and behavior. Implementing software module clustering with optimal results is challenging. Accordingly, researchers have addressed many aspects of software module clustering in the past decade. Thus, it is essential to present the research evidence that has been published in this area. In this study, 143 research papers from well-known literature databases that examined software module clustering were reviewed to extract useful data. The obtained data were then used to answer several research questions regarding state-of-the-art clustering approaches, applications of clustering in software engineering, clustering processes, clustering algorithms, and evaluation methods. Several research gaps and challenges in software module clustering are discussed in this paper to provide a useful reference for researchers in this field

A test case generation framework based on UML statechart diagram

Early software fault detection offers more flexibility to correct errors in the early development stages. Unfortunately, existing studies in this domain are not sufficiently comprehensive in describing the major processes of the automated test case generation. Furthermore, the algorithms used for test case generation are not provided or well described. Current studies also hardly address loops and parallel paths issues, and achieved low coverage criteria. Therefore, this study proposes a test case generation framework that generates minimized and prioritized test cases from UML statechart diagram with higher coverage criteria. This study, conducted a review of the previous research to identify the issues and gaps related to test case generation, model-based testing, and coverage criteria. The proposed framework was designed from the gathered information based on the reviews and consists of eight components that represent a comprehensive test case generation processes. They are relation table, relation graph, consistency checking, test path minimization, test path prioritization, path pruning, test path generation, and test case generation. In addition, a prototype to implement the framework was developed. The evaluation of the framework was conducted in three phases: prototyping, comparison with previous studies, and expert review. The results reveal that the most suitable coverage criteria for UML statechart diagram are all-states coverage, all-transitions coverage, alltransition-pairs coverage, and all-loop-free-paths coverage. Furthermore, this study achieves higher coverage criteria in all coverage criteria, except for all-state coverage, when compared with the previous studies. The results of the experts’ review show that the framework is practical, easy to implement due to it is suitability to generate the test cases. The proposed algorithms provide correct results, and the prototype is able to generate test case effectively. Generally, the proposed system is well accepted by experts owing to its usefulness, usability, and accuracy. This study contributes to both theory and practice by providing an early alternative test case generation framework that achieves high coverage and can effectively generate test cases from UML statechart diagrams. This research adds new knowledge to the software testing field, especially for testing processes in the model-based techniques, testing activity, and testing tool support

Performance evaluation metrics for multi-objective evolutionary algorithms in search-based software engineering: Systematic literature review

Many recent studies have shown that various multi-objective evolutionary algorithms have been widely applied in the field of search-based software engineering (SBSE) for optimal solutions. Most of them either focused on solving newly re-formulated problems or on proposing new approaches, while a number of studies performed reviews and comparative studies on the performance of proposed algorithms. To evaluate such performance, it is necessary to consider a number of performance metrics that play important roles during the evaluation and comparison of investigated algorithms based on their best-simulated results. While there are hundreds of performance metrics in the literature that can quantify in performing such tasks, there is a lack of systematic review conducted to provide evidence of using these performance metrics, particularly in the software engineering problem domain. In this paper, we aimed to review and quantify the type of performance metrics, number of objectives, and applied areas in software engineering that reported in primary studies-this will eventually lead to inspiring the SBSE community to further explore such approaches in depth. To perform this task, a formal systematic review protocol was applied for planning, searching, and extracting the desired elements from the studies. After considering all the relevant inclusion and exclusion criteria for the searching process, 105 relevant articles were identified from the targeted online databases as scientific evidence to answer the eight research questions. The preliminary results show that remarkable studies were reported without considering performance metrics for the purpose of algorithm evaluation. Based on the 27 performance metrics that were identified, hypervolume, inverted generational distance, generational distance, and hypercube-based diversity metrics appear to be widely adopted in most of the studies in software requirements engineering, software design, software project management, software testing, and software verification. Additionally, there are increasing interest in the community in re-formulating many objective problems with more than three objectives, yet, currently are dominated in re-formulating two to three objectives

Optimizing the selection of architecture for component-based system

Redundant components are commonly used for solving Redundancy Allocation Problems (RAP) and improving the reliability of complex systems. However, the use of such a strategy to minimize development costs while maintaining high quality attributes for building software architecture is a research challenge. The selection for an optimal architecture to meet this challenge is an inherently complex task due to the high volume of possible architectural candidates and the fundamental conflict between quality attributes. Current software evaluation methods focus on predicting the quality attributes and selecting Commercial-Off-the Shelf (COTS) components for COTS-Based applications rather than utilizing additional architectural evaluation methods that could increase the opportunity for obtaining a cost-effective solution for RAP. In this thesis, an architecture-based approach called Cost-Discount and Build-or-Buy for RAP (CD/BoB-RAP) is introduced to support the decision making for selecting the architecture with optimal components and level of redundancy that satisfies the technical and financial preferences. This approach consists of an optimization model that includes two architectural evaluation methods (CD-RAP and BoB-RAP) and applies three variants of Particle Swarm Optimization (PSO) algorithms. Statistical results showed a 74% reduction on the development cost using CD-RAP on an embedded system case study. Moreover, the application of a maximum possible improvement on the algorithms showed that Penalty Guided PSO (PG-PSO) had enhanced the quality of obtained solutions by 70% to 84% in comparison to other algorithms. The results of the CD-RAP and BoB-RAP were superior when compared to the results obtained from similar approaches. The overall results of this research have proven the potential benefits of the CD/BoB-RAP approach for software architecture evaluation, particularly, in selecting software architecture for minimizing the development cost maintaining a highly reliable system

SusTrainable: Promoting Sustainability as a Fundamental Driver in Software Development Training and Education. 2nd Teacher Training, January 23-27, 2023, Pula, Croatia. Revised lecture notes

This volume exhibits the revised lecture notes of the 2nd teacher training organized as part of the project Promoting Sustainability as a Fundamental Driver in Software Development Training and Education, held at the Juraj Dobrila University of Pula, Croatia, in the week January 23-27, 2023. It is the Erasmus+ project No. 2020-1-PT01-KA203-078646 - Sustrainable. More details can be found at the project web site https://sustrainable.github.io/ One of the most important contributions of the project are two summer schools. The 2nd SusTrainable Summer School (SusTrainable - 23) will be organized at the University of Coimbra, Portugal, in the week July 10-14, 2023. The summer school will consist of lectures and practical work for master and PhD students in computing science and closely related fields. There will be contributions from Babe\c{s}-Bolyai University, E\"{o}tv\"{o}s Lor\'{a}nd University, Juraj Dobrila University of Pula, Radboud University Nijmegen, Roskilde University, Technical University of Ko\v{s}ice, University of Amsterdam, University of Coimbra, University of Minho, University of Plovdiv, University of Porto, University of Rijeka. To prepare and streamline the summer school, the consortium organized a teacher training in Pula, Croatia. This was an event of five full days, organized by Tihana Galinac Grbac and Neven Grbac. The Juraj Dobrila University of Pula is very concerned with the sustainability issues. The education, research and management are conducted with sustainability goals in mind. The contributions in the proceedings were reviewed and provide a good overview of the range of topics that will be covered at the summer school. The papers in the proceedings, as well as the very constructive and cooperative teacher training, guarantee the highest quality and beneficial summer school for all participants.Comment: 85 pages, 8 figures, 3 code listings and 1 table; editors: Tihana Galinac Grbac, Csaba Szab\'{o}, Jo\~{a}o Paulo Fernande