A Survey on the Interplay between Software Engineering and Systems Engineering during SoS Architecting

Background: The Systems Engineering and Software Engineering disciplines are highly intertwined in most modern Systems of Systems (SoS), and particularly so in industries such as defense, transportation, energy and health care. However, the combination of these disciplines during the architecting of SoS seems to be especially challenging; the literature suggests that major integration and operational issues are often linked to ambiguities and gaps between system-level and software-level architectures. Aims: The objective of this paper is to empirically investigate: 1) the state of practice on the interplay between these two disciplines in the architecting process of systems with SoS characteristics; 2) the problems perceived due to this interplay during said architecting process; and 3) the problems arising due to the particular characteristics of SoS systems. Method: We conducted a questionnaire-based online survey among practitioners from industries in the aforementioned domains, having a background on Systems Engineering, Software Engineering or both, and experience in the architecting of systems with SoS characteristics. The survey combined multiple-choice and open-ended questions, and the data collected from the 60 respondents were analyzed using quantitative and qualitative methods. Results: We found that although in most cases the software architecting process is governed by system-level requirements, the way requirements were specified by systems engineers, and the lack of domain-knowledge of software engineers, often lead to misinterpretations at software level. Furthermore, we found that unclear and/or incomplete specifications could be a common cause of technical debt in SoS projects, which is caused, in part, by insufficient interface definitions. It also appears that while the SoS concept has been adopted by some practitioners in the field, the same is not true about the existing and growing body of knowledge on the subject in Software Engineering resulting in recurring problems with system integration. Finally, while not directly related to the interplay of the two disciplines, the survey also indicates that low-level hardware components, despite being identified as the root cause of undesired emergent behavior, are often not considered when modeling or simulating the system. Conclusions: The survey indicates the need for tighter collaboration between the two disciplines, structured around concrete guidelines and practices for reconciling their differences. A number of open issues identified by this study require further investigation

Automatic generation of software interfaces for supporting decision-making processes. An application of domain engineering and machine learning

Information dashboards are sophisticated tools. Although they enable users to reach useful insights and support their decisionmaking challenges, a good design process is essential to obtain powerful tools. Users need to be part of these design processes, as they will be the consumers of the information displayed. But users are very diverse and can have different goals, beliefs, preferences, etc., and creating a new dashboard for each potential user is not viable. There exist several tools that allow users to configure their displays without requiring programming skills. However, users might not exactly know what they want to visualize or explore, also becoming the configuration process a tedious task. This research project aims to explore the automatic generation of user interfaces for supporting these decisionmaking processes. To tackle these challenges, a domain engineering, and machine learning approach is taken. The main goal is to automatize the design process of dashboards by learning from the context, including the end-users and the target data to be displayed

systematic review of statistical process control an experience report

Background: A systematic review is a rigorous method for assessing and aggregating research results. Unlike an ordinary literature review consisting of an annotated bibliography, a systematic review analyzes existing literature with reference to specific research questions on a topic of interest. Objective: Statistical Process Control (SPC) is a well established technique in manufacturing contexts that only recently has been used in software production. Software production is unlike manufacturing because it is human rather than machine-intensive, and results in the production of single one-off items. It is therefore pertinent to assess how successful SPC is in the context of software production. These considerations have therefore motivated us to define and carry out a systematic review to assess whether SPC is being used effectively and correctly by software practitioners. Method: A protocol has been defined, according to the systematic literature review process, it was revised and refined by the authors. At the current time, the review is being carried out. Results: We report our considerations and preliminary results in defining and carrying out a systematic review on SPC, and how graduate students have been included in the review process of a first set of the papers. Conclusions: Our first results and impressions are positive. Also, involving graduate students has been a successful experience

Systematic literature review on user logging in virtual reality

In this systematic literature review, we study the role of user logging in virtual reality research. By categorizing literature according to data collection methods and identifying reasons for data collection, we aim to find out how popular user logging is in virtual reality research. In addition, we identify publications with detailed descriptions about logging solutions. Our results suggest that virtual reality logging solutions are relatively seldom described in detail despite that many studies gather data by body tracking. Most of the papers gather data to witness something about a novel functionality or to compare different technologies without discussing logging details. The results can be used for scoping future virtual reality research.acceptedVersionPeer reviewe