Exploring Maintainability Assurance Research for Service- and Microservice-Based Systems: Directions and Differences

To ensure sustainable software maintenance and evolution, a diverse set of activities and concepts like metrics, change impact analysis, or antipattern detection can be used. Special maintainability assurance techniques have been proposed for service- and microservice-based systems, but it is difficult to get a comprehensive overview of this publication landscape. We therefore conducted a systematic literature review (SLR) to collect and categorize maintainability assurance approaches for service-oriented architecture (SOA) and microservices. Our search strategy led to the selection of 223 primary studies from 2007 to 2018 which we categorized with a threefold taxonomy: a) architectural (SOA, microservices, both), b) methodical (method or contribution of the study), and c) thematic (maintainability assurance subfield). We discuss the distribution among these categories and present different research directions as well as exemplary studies per thematic category. The primary finding of our SLR is that, while very few approaches have been suggested for microservices so far (24 of 223, ?11%), we identified several thematic categories where existing SOA techniques could be adapted for the maintainability assurance of microservices

User experience in social robots

Social robots are increasingly penetrating our daily lives. They are used in various domains, such as healthcare, education, business, industry, and culture. However, introducing this technology for use in conventional environments is not trivial. For users to accept social robots, a positive user experience is vital, and it should be considered as a critical part of the robots’ development process. This may potentially lead to excessive use of social robots and strengthen their diffusion in society. The goal of this study is to summarize the extant literature that is focused on user experience in social robots, and to identify the challenges and benefits of UX evaluation in social robots. To achieve this goal, the authors carried out a systematic literature review that relies on PRISMA guidelines. Our findings revealed that the most common methods to evaluate UX in social robots are questionnaires and interviews. UX evaluations were found out to be beneficial in providing early feedback and consequently in handling errors at an early stage. However, despite the importance of UX in social robots, robot developers often neglect to set UX goals due to lack of knowledge or lack of time. This study emphasizes the need for robot developers to acquire the required theoretical and practical knowledge on how to perform a successful UX evaluation.publishedVersio

Efficiently Manifesting Asynchronous Programming Errors in Android Apps

Android, the #1 mobile app framework, enforces the single-GUI-thread model, in which a single UI thread manages GUI rendering and event dispatching. Due to this model, it is vital to avoid blocking the UI thread for responsiveness. One common practice is to offload long-running tasks into async threads. To achieve this, Android provides various async programming constructs, and leaves developers themselves to obey the rules implied by the model. However, as our study reveals, more than 25% apps violate these rules and introduce hard-to-detect, fail-stop errors, which we term as aysnc programming errors (APEs). To this end, this paper introduces APEChecker, a technique to automatically and efficiently manifest APEs. The key idea is to characterize APEs as specific fault patterns, and synergistically combine static analysis and dynamic UI exploration to detect and verify such errors. Among the 40 real-world Android apps, APEChecker unveils and processes 61 APEs, of which 51 are confirmed (83.6% hit rate). Specifically, APEChecker detects 3X more APEs than the state-of-art testing tools (Monkey, Sapienz and Stoat), and reduces testing time from half an hour to a few minutes. On a specific type of APEs, APEChecker confirms 5X more errors than the data race detection tool, EventRacer, with very few false alarms

Software developers reasoning behind adoption and use of software development methods – a systematic literature review

When adopting and using a Software Development Method (SDM), it is important to stay true to the philosophy of the method; otherwise, software developers might execute activities that do not lead to the intended outcomes. Currently, no overview of SDM research addresses software developers’ reasoning behind adopting and using SDMs. Accordingly, this paper aims to survey existing SDM research to scrutinize the current knowledge base on software developers’ type of reasoning behind SDM adoption and use. We executed a systematic literature review and analyzed existing research using two steps. First, we classified papers based on what type of reasoning was addressed regarding SDM adoption and use: rational, irrational, and non-rational. Second, we made a thematic synthesis across these three types of reasoning to provide a more detailed characterization of the existing research. We elicited 28 studies addressing software developers’ reasoning and identified five research themes. Building on these themes, we framed four future research directions with four broad research questions, which can be used as a basis for future research

Usability framework for mobile augmented reality language learning

After several decades since its introduction, the existing ISO9241-11 usability framework is still vastly used in Mobile Augmented Reality (MAR) language learning. The existing framework is generic and can be applied to diverse emerging technologies such as electronic and mobile learning. However, technologies like MAR have interaction properties that are significantly unique and require different usability processes. Hence, implementing the existing framework on MAR can lead to non-optimized, inefficient, and ineffective outcomes. Furthermore, state-of-the-art analysis models such as machine learning are not apparent in MAR usability studies, despite evidence of positive outcomes in other learning technologies. In recent MAR learning studies, machine learning benefits such as problem identification and prioritization were non-existent. These setbacks could slow down the advancement of MAR language learning, which mainly aims to improve language proficiency among MAR users, especially in English communication. Therefore, this research proposed the Usability Framework for MAR (UFMAR) that addressed the currently identified research problems and gaps in language learning. UFMAR introduced an improved data collection method called Individual Interaction Clustering-based Usability Measuring Instrument (IICUMI), followed by a machine learning-driven analysis model called Clustering-based Usability Prioritization Analysis (CUPA) and a prioritization quantifier called Usability Clustering Prioritization Model (UCPM). UFMAR showed empirical evidence of significantly improving usability in MAR, capitalizing on its unique interaction properties. UFMAR enhanced the existing framework with new abilities to systematically identify and prioritize MAR usability issues. Through the experimental results of UFMAR, it was found that the IICUMI method was 50% more effective, while CUPA and UCPM were 57% more effective than the existing framework. The outcome through UFMAR also produced 86% accuracy in analysis results and was 79% more efficient in framework implementation. UFMAR was validated through three cycles of the experimental processes, with triangulation through expert reviews, to be proven as a fitting framework for MAR language learning

The development building maintenance training model of healthcare industry

This thesis deals with investigated the building maintenance aspects in maintenance organization of healthcare industry in Malaysia. The aim is to develop a model for building maintenance training for health care industry. The intention of this qualitative study was to generate a theory, grounded in data.The purpose of this study was to develop a theory, grounded in data that conceptualizes the main concern of building maintenance. The main contribution of the thesis was to generate new model for training in building maintenance for healthcare industry. The study contributes to the body of knowledge for training development in building maintenance for healthcare industry. This research was conducted using Grounded Theory approach. The methods of initial data collection were: face-to-face interviews with the technical managers and technical supervisors. The Grounded Theory requirements, the participants were selected using purposive sampling method taken from the list of workers who have experienced the maintenance works which were held during preliminary study. The study applies Grounded Theory as an holistic methodology to investigate the experience of building maintenance practitioners in this study context. Grounded Theory is a sociological methodology designed to formulate a new (Grounded) theory from a substantive area‘, i.e. a participant group typically comprising a shared technical role or activity. Key elements of Grounded Theory include an emphasis on induction-based conceptualization of theory from descriptive participant indicators and the continuous comparison of data for the emergence of ‗coding categories‘ due to thematic analysis. Using core grounded theory concepts, a methodological framework of data collection and analysis was developed that focused on data centrality and discovering a data- emergent theory grounded within the research field. A core category of selective perception emerged that explained and captured the core phenomenon of sustained barriers to decision-making and selective bias towards information due to the interpretative nature of the socially constructed environment. At the core of the discovered theory is that individuals have a tendency to reject decisions within an informal environment based on external variables not directly related to the decisions. By constructing a theoretical model explained through several propositions, this thesis shows that decision-making efficiency is impacted by selective perception, communication effectiveness, the level of trust, and available resources, with a strong interrelation between each variable. The concept of adaptability was applied and tested for relevance and effectiveness within the research field, with positive results. Grounded Theory by Glaser & Strauss was employed to investigate this phenomenon thus revealing the main concern of the participants and resulting in specific theoretical propositions grounded in the data. The building maintenance practitioner‘s interviews in this study identified building maintenance key points relating to employee experiences of work-life interaction. The research focus was then narrowed to delimit the emerging theory around four main categories in the data. In total, interviews were conducted as part of the dynamic and fluid process of coding, theoretical sampling, literature review and interpretation that is Grounded Theory. This thesis concludes with discussions on the implications for practice, research, and suggestions for future research. As summary, the development of building maintenance model for in-house training of maintenance healthcare industry comprises of integrated component such as architectural, building services and safety and health thus ‗BIMO‘ model as contribution by researcher towards to knowledge and society

A systematic review of perception system and simulators for autonomous vehicles research

This paper presents a systematic review of the perception systems and simulators for autonomous vehicles (AV). This work has been divided into three parts. In the first part, perception systems are categorized as environment perception systems and positioning estimation systems. The paper presents the physical fundamentals, principle functioning, and electromagnetic spectrum used to operate the most common sensors used in perception systems (ultrasonic, RADAR, LiDAR, cameras, IMU, GNSS, RTK, etc.). Furthermore, their strengths and weaknesses are shown, and the quantification of their features using spider charts will allow proper selection of different sensors depending on 11 features. In the second part, the main elements to be taken into account in the simulation of a perception system of an AV are presented. For this purpose, the paper describes simulators for model-based development, the main game engines that can be used for simulation, simulators from the robotics field, and lastly simulators used specifically for AV. Finally, the current state of regulations that are being applied in different countries around the world on issues concerning the implementation of autonomous vehicles is presented.This work was partially supported by DGT (ref. SPIP2017-02286) and GenoVision (ref. BFU2017-88300-C2-2-R) Spanish Government projects, and the “Research Programme for Groups of Scientific Excellence in the Region of Murcia" of the Seneca Foundation (Agency for Science and Technology in the Region of Murcia – 19895/GERM/15)

Mining Architectural Information: A Systematic Mapping Study

Context: Mining Software Repositories (MSR) has become an essential activity in software development. Mining architectural information to support architecting activities, such as architecture understanding and recovery, has received a significant attention in recent years. However, there is an absence of a comprehensive understanding of the state of research on mining architectural information. Objective: This work aims to identify, analyze, and synthesize the literature on mining architectural information in software repositories in terms of architectural information and sources mined, architecting activities supported, approaches and tools used, and challenges faced. Method: A Systematic Mapping Study (SMS) has been conducted on the literature published between January 2006 and November 2021. Results: Of the 79 primary studies finally selected, 8 categories of architectural information have been mined, among which architectural description is the most mined architectural information; 12 architecting activities can be supported by the mined architectural information, among which architecture understanding is the most supported activity; 81 approaches and 52 tools were proposed and employed in mining architectural information; and 4 types of challenges in mining architectural information were identified. Conclusions: This SMS provides researchers with promising future directions and help practitioners be aware of what approaches and tools can be used to mine what architectural information from what sources to support various architecting activities.Comment: 68 pages, 5 images, 15 tables, Manuscript submitted to a Journal (2022