This artifact accompanies the SEET@ICSE article "Assessing the impact of hints in learning formal specification", which reports on a user study to investigate the impact of different types of automated hints while learning a formal specification language, both in terms of immediate performance and learning retention, but also in the emotional response of the students. This research artifact provides all the material required to replicate this study (except for the proprietary questionnaires passed to assess the emotional response and user experience), as well as the collected data and data analysis scripts used for the discussion in the paper. Dataset The artifact contains the resources described below. Experiment resources The resources needed for replicating the experiment, namely: alloy_sheet_pt.pdf: the 1-page Alloy sheet that participants had access to during the 2 sessions of the experiment. The sheet was passed in Portuguese due to the population of the experiment. alloy_sheet_en.pdf: a version the 1-page Alloy sheet that participants had access to during the 2 sessions of the experiment translated into English. task_permalinks.txt: a list of example permalinks pointing to the tasks that participants had to solve in the 2 sessions of the experiment. Collected data Data collected in the simple one-factor randomised experiment in 2 sessions involving 85 undergraduate students majoring in CSE. The experiment was validated by the Specialised Committee for Research in the Social Sciences and Humanities of the University of Minho, where the experiment took place. Data is shared the shape of CSV files with a header row, namely: task_data.csv: main data collected from task-solving in the 2 sessions of the experiment, namely: participant identification: participant's unique identifier (ID) and the assigned treatment (column HINT, either N, L, E or D); sociodemographic information: participant's age (AGE), sex (SEX, 1 through 4 for female, male, prefer not to disclosure, and other, respectively), and average academic grade (GRADE, from 0 to 20, NA denotes preference to not disclosure); overall task resolution: task resolution data in the two sessions, namely variables productivity (PROD1 and PROD2, between 0 and 12 solved tasks) and efficiency (EFF1 and EFF2, between 0 and 1); the number of tries is also provided (TRY1 and TRY2), from which efficiency was calculated; task resolution per domain model: the task resolution data from the 1st session but split by domain model (suffix A for the social network and suffix B for course management), which was used to discuss a possible threat to internal validity in Section 5 of the paper; emotional response data: summarised emotional response data calculated from the PrEmo 2 surveys (EMO1 and EMO2, sum of differential in positive and negative emotions, ranging from -14 to 14, NA denotes failure to submit the questionnaire); the negative (NEG1 and NEG2, from -7 to 7) and positive differentials (POS1 and POS2, from -7 to 7) summed in that metric are also provided; user experience data: summarised user experience data from the UMUX surveys (UMUX1 and UMUX2, as a usability metric ranging from 0 to 100). emotional_data.csv: detailed data collected from the emotional questionnaire in the 2 sessions, namely: participant identification: participant's unique identifier (ID) and the assigned treatment (column HINT, either N, L, E or D); detailed emotional response data: the differential in each of the 14 measured emotions in the 2 sessions, half of which positive (Admiration1 and Admiration2, Desire1 and Desire2, Hope1 and Hope2, Fascination1 and Fascination2, Joy1 and Joy2, Satisfaction1 and Satisfaction2, and Pride1 and Pride2, either -1 if decreased, 0 if maintained, 1 if increased, NA denotes failure to submit the questionnaire), and half of which negative (Anger1 and Anger2, Boredom1 and Boredom2, Contempt1 and Contempt2, Disgust1 and Disgust2, Fear1 and Fear2, Sadness1 and Sadness2, and Shame1 and Shame2, also either -1, 0 or 1), used in the detailed discussion in Section 6 of the paper. Analysis scripts The analysis scripts required to replicate the results reported in the paper, namely: analysis.r: An R script to analyse the data in the provided CSV file, the results of which are reported in the paper; each performed analysis is documented within the file itself. Setup Running the analysis scripts requires the installation of the R software. R is freely available for download at https://cran.r-project.org/mirrors.html, with binary distributions available for Windows, Linux and Mac. The required R libraries will be installed by the script if needed. Usage After installing the R software, just run the script through the command line to get the result of the statistical tests. In particular, for Linux and Mac, you can just run Rscript analysis.r and for Windows Rscript.exe analysis.r The data analysis results are reported in the terminal by default. For more details on running R, you can consult https://cran.r-project.org/doc/manuals/r-release/R-intro.html#Invoking-R.
EXtended Reality (XR) has numerous applications in industrial contexts, including to assist in activities such as preventive maintenance and/or reactive interventions in machinery, wherein time-effectiveness is required to mitigate the costs of inactive production lines or the need to subcontract specialized technicians to perform those operations. However, developing an Augmented Reality (AR) application capable of being user-friendly and truly serviceable for aiding in the referred context requires keeping in mind human factors as key aspects and, as such, interaction must be a concern to address, namely, considering user feedback. Therefore, starting from a previous AR system oriented to industrial interventions that was developed and assessed with users and considering the feedback of the participant, the duly alterations were done and a new test was conducted, with a total of 27 participants, divided into 2 groups: 11 users who participated in the first experience and 16 general users who had never contacted with the solution before. A qualitative improvement in the user experience was noticed from the questionnaires feedback. Points such as fluidity, responsiveness, ease, speed, and intuitiveness were highlighted. Voice commands matched the tap commands in terms of user preferences.
Companies have invested in Domain-Driven Design to support data-driven business, but this strategy has led to bottlenecks, demanding the decentralisation of analytical data by domain. This paper proposes a converged strategy for a domain-specific decentralisation of analytical data. Sequentially applied, the elicitation of cloud computing microservices, their identification uses using functionally decomposed UML use cases as a result of the domain-driven design, the application of a logical architecture derivation method followed by the application of microservices patterns, and, finally, the application of data mesh principles to organize packets or reorganize objects in new packet boundaries, result in the derivation of a converged architecture containing both microservices quantum and data mesh quantum as a distributed architecture mesh.
The increasingly standards of the electronic industry, especially in Printed Circuit Boards, make In-Circuit-Test Machines one of the most important systems on the production lines. With this, the pressure on the In-Circuit-Test Machines production to meet this strict requirements it is getting bigger every day. To react to this pressure it is necessary to update, in a technological point of view, the manufacturing process of the In-Circuit-Test Machines, particularly its needles bed. To do so it is necessary to automatize the process. This automatization of the process was divided into several sub-functions, being one of them, the vision System. This system is the focus of this paper. The problem was analyzed, several solutions were studied within the state of the art and one original solution was proposed to solve the problem. The decision process followed engineering criteria’s and culminated on the proposal of a solution to solve the identified problem. Applying engineering tools to understand which approach is better when implementing a Vision Systems proved to be a good method.
Industry 4.0 provides intelligent factories, intelligent processes, and cyber-physical systems. Systems of the future will have to be able to handle adversities autonomously. Nowadays, engineering practices are increasingly distributed and decentralized, thus causing challenges to the level of interoperability between the various systems developed. Regardless of the structure of the databases, it is necessary to have a mechanism that guarantees the interoperability between these systems. In this paper, we present two types of integrations through ontologies: vertical integration that is a way to achieve semantic interoperability between industrial plant, MES, and ERP and horizontal integration to achieve interoperability throughout the product lifecycle. Finally, this interoperability contribution was crucial to develop an asset efficiency system.
