The teaching learning-based optimization (TLBO) algorithm has shown competitive performance in solving numerous real-world optimization problems. Nevertheless, this algorithm requires better control for exploitation and exploration to prevent premature convergence (i.e., trapped in local optima), as well as enhance solution diversity. Thus, this paper proposes a new TLBO variant based on Mamdani fuzzy inference system, called ATLBO, to permit adaptive selection of its global and local search operations. In order to assess its performances, we adopt ATLBO for the mixed strength t-way test generation problem. Experimental results reveal that ATLBO exhibits competitive performances against the original TLBO and other meta-heuristic counterparts.The teaching learning-based optimization algorithm (TLBO) [6,7] adopts a simplistic approach of disregarding the control parameters (i.e., parameter free). TLBO specifically performs both global and local search sequentially per iteration to balance exploration and exploitation. Given that exploration and exploitation are dynamic in nature depending on the current search space region, any preset division between the two can be counter-productive and may lead to poor quality solutions. This paper addresses these issues through a new TLBO variant, adaptive TLBO (ATLBO) integrated with the Mamdani-type fuzzy inference system [8,9]. ATLBO adaptively selects its local and global search operations. In order to assess its performances, we adopt ATLBO for the mixed strength t-way test generation problem.Our contributions are summarized as follows:The novel ATLBO strategy based on the Mamdani-type fuzzy inference system is presented for exploration (i.e., global search) and exploitation (i.e., local search) selection. ATLBO is the first TLBO-variant strategy that addresses generation for both uniform and mixedstrength t-way test suite.This study is organized as follows. Section 2 presents the theoretical framework that covers the generation problem of t-way test and its mathematical notation. Section 3 describes the related work. Section 4 highlights the original TLBO algorithm and its variants, along with its applications. Section 5 outlines the novel ATLBO. Benchmark experiments are presented in Section 6. Section 7 and 8 discusses the experimental observations and validity threats, respectively. Finally, Section 9 concludes this study and presents the scope for future work. Covering Array (CA) and the Generation Problem of Mixed-Strength t-way TestThe generation problem of t-way test is often associated with CA notation, where t represents the desired interaction strength. A CA (N; t, p, v), which is also expressed as CA (N; t, v p ), is a combinatorial structure constructed as an array of N rows and p columns (i.e., parameters) on v values, such that every N × t sub-array contains all ordered subsets from the v values of size t at least once [10]. When the number of component values varies, this condition can be handled by a mixed CA (MCA) (N; t, p, (v 1 , v 2 , …v i )) or MCA (N;...

Software architecture (SA) has a prominent role in all stages of system development. Given the persistent evolution of software systems over time, SA tends to be eroded or degraded. Such phenomenon is called architectural degradation. In light of this phenomenon, the current study focuses on problems of architectural erosion in the open-source software (OSS). There has been a significant research activity on the OSS over the last decade. Nonetheless, the architectural degradation problems in the OSS are still scattered and disorganized. In addition, there has been no systematic attempt made on existing studies to provide evidence, insight and better understanding for researchers and practitioners. The main objective of the present study is to provide a profound understanding and to review the existing studies on the architectural erosion of the OSS. In this study, we conduct a systematic literature review (SLR) to gather, organize, classify, and analyze the architectural degradation of previous papers published until the year 2020. The data for this study were collected from eight major online databases (ACM, Springer, ScienceDirect, Taylor, IEEE Explorer, Scopus, Web of Science, and Wiley). A total of 74 primary studies were identified as the final samples of this research. The results indicated that rapid software evolution, frequent changes, and the lack of developers' awareness are the most common causes occurred in architecture degradation. Meanwhile, the prominent key indicators of architectural erosion symptoms are code smells and architectural smells. Additionally, the results indicated the most commonly used of the proposed solution for addressing architectural erosion is the metrics-based strategy. Acknowledging the limitations of the current study, more studies are needed that focus on determining other causes that are still ambiguous and improving the other solutions to provide better results in the precision and effectiveness of addressing architectural erosion.

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This paper describes the test oracle generation from an abstraction relation document that is documented using Parnas's Module Documentation (MD) method. This work is part of ongoing research that addresses the problem of improving the effectiveness of fault detection. We focus our work on unit/module testing where each module may consist of several programs. The aim of our project is to investigate the strategies and techniques to automate module testing. In particular, we investigate the use of MD that is written in standard mathematical notation to automate the process of test oracle generation and test execution.

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Software architecture is crucial in determining success or failure in a variety of software development and design fields. Typically, as a system evolves, software architecture deteriorates. This phenomenon is known as architectural erosion. Several studies have addressed architectural erosion based on different solutions. As a result, the metrics technique is the most prevalent solution for architectural erosion. Nevertheless, a comprehensive description of architectural erosion metrics remains unorganized and scattered. This work aims to conduct a systematic mapping to describe and analyze the architectural erosion metrics to provide an overview of erosion metrics and their current trends. Furthermore, no systematic attempts have been made on architectural erosion metrics. The final samples of this study were specified as a total of 43 included papers. Nearly 100 architectural erosion metrics were found. We proposed nine classifications to address architectural erosion challenges, based on adopted approaches in primary studies. The metrics of architectural erosion provide strong evidence for identifying decay and a rapid enabler factor for the adoption of numerous metrics mechanisms to address architectural erosion. The classification of metrics, which is the first of its kind, benefits researchers and practitioners. However, it can be concluded that various aspects are still ambiguous and require further research on architectural erosion measures.

a b s t r a c tContext: Testing a module that has memory using the black-box approach has been found to be expensive and relatively ineffective. Instead, testing without knowledge of the specifications (white-box approach) may not be effective in showing whether a program has been properly implemented as stated in its specifications. We propose instead a grey-box approach called Module Documentation-based Testing or MD-Test, the heart of which is an automatic generation of the test oracle from the external and internal views of the module. Objective: This paper presents an empirical analysis and comparison of MD-Test against three existing testing tools. Method: The experiment was conducted using a mutation-testing approach, in two phases that assess the capability of MD-Test in general and its capability of evaluating test results in particular. Results: The results of the general assessment indicate that MD-Test is more effective than the other three tools under comparison, where it is able to detect all faults. The second phase of the experiment, which is significant to this study, compares the capabilities of MD-Test and JUnit-black using the test evaluation results. Likewise, an analysis of the test evaluation results shows that MD-Test is more effective and efficient, where MD-Test is able to detect at least the same number of faults as, or is at par with, the black-box approach. Conclusion: It is concluded that test evaluation using grey-box approach is more effective and efficient that the black-box approach when testing a module that has memory.

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.