Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
192
datasets available to search
ShareScore release 0.9.0
Dataset results
192 results for “software engineering”
Do we use FLOSS in Software Engineering Education? Mapping the Profiles and Practices of Higher Education Teachers from Brazi
<p>Context:Software Engineering (SE) is a key topic in undergraduate computing-related courses that provides the basic knowledge and skills necessary for professional practice in the software industry. Teaching SE principles, concepts and practices and relating them to real-world scenarios are challenging tasks, and the adoption of Free/Libre/Open Source Software (FLOSS) projects can help to face these challenges. On the other hand, using FLOSS projects as a didactic resource may introduce additional challenges to professors that are not familiar with the FLOSS ecosystem. Objective: This research aims to identify and map the profiles of professors of SE courses in Brazil, as well as to present the pedagogical practices used in the experience with FLOSS projects in Software Engineering Education (SEE). Method: We performed a survey with higher education professors in Brazil, used K-modes algorithm to identify clusters and Decision Tree algorithm to identify characteristics that determine the use of FLOSS projects in a sample of professors who had not used this approach in the classroom.Results:The results of the research revealed characteristics of professors who use, or not,FLOSS projects in SEE, of professors grouped in the two clusters generated by the application of the K-modes algorithm, of professors grouped by the application of the Decision Tree algorithm,in addition to presenting similar characteristics the pedagogical practices evidenced by each group of SE professors.</p>
Competencies Development based on Thinking-based Learning in Software Engineering: An Action-Research
<p>For a long time, the teaching of Software Engineering (SE) has been carried out in a traditional way without taking into account relevant aspects of the student’s personality reflected in their learning. There is still a lack of research focused on SE that promotes adequate teaching methods so that new generations of students and future professionals have a humanistic, critical and reflexive formation. A novel teaching method called Thinking-based Learning (TBL) was proposed to develop effective thinking in students using thinking skills, habits of the mind and the metacognition during the teaching of subject content. The action research as a methodology to improve their teaching practices in education has been seen as a positive change in educational practices. The aim of this research was to perform an action research based on TBL method to assist in the development of competencies that are less attended by traditional methods currently used during the teaching and learning process of students in SE. Moreover, a Systematic Literature Review (SLR) was conducted to identify gaps in the contribution of teaching methods in SE until the present. The data are obtained by comparing the competencies achieved with TBL with the traditional methods previously used in the discipline, and additionally with the data obtained from the SLR. The results indicated that critical thinking, autonomy, problem solving and creativity were the most developed competencies by students during the course period. We are planning to expand the TBL and applying it in other disciplines of the same course as well as in other research areas to determine its functionality and interdisciplinarity. We hope that this experience with TBL will encourage the development of competencies among SE teachers.</p>
Software Engineering Meets Deep Learning: A Mapping Study
<p>Dataset used in the paper "Software Engineering Meets Deep Learning: A Mapping Study"</p>
Assessing Iterative Practical Software Engineering Courses with Play Money (Raw data of survey)
<p>This is the raw data of the surveys conducted for a paper / poster " Assessing Iterative Practical Software Engineering Courses with Play Money" at the ICSE 2016.</p>
Dataset for survey of industry-academia collaboration in software engineering (phase 1)
<p>Dataset for survey of industry-academia collaboration in software engineering (phase 1)</p>
Questions and Answers of a Survey Related to the Use of Dublin Core to Register Metadata Generated in Software Engineering Experiments.
<p>Questions were presented and answers were collected during the application of the survey.</p>
Supplemental package of a study on the implications of AI-based tools for the human aspects of software engineering
Open the record for dataset details and reuse information.
Survey on Experimental Software Engineering Process - Answers
Open the record for dataset details and reuse information.
Not real or too soft? On the challenges of publishing interdisciplinary software engineering research (supplementary material)
<p>Supplementary material for paper "Not real or too soft? On the challenges of publishing interdisciplinary software engineering research" (ICSE 2025 SEIS track).</p>
Questions for survey of industry-academia collaborations in software engineering
<p>Questions for survey of industry-academia collaborations in software engineering</p>
Statistical Errors in Experimental Software Engineering: Possible Causes and Recommended Solutions
<p>to define</p>
Assessing diversity in creating seed set for snowballing search for systematic literature review in software engineering
<p><strong><span>This set of files is complementary material used to evaluate the snowballing performance varying the seed set creation under the diversity perspective from the proposed tool. Specifically, the set of files includes a PDF with guidelines followed to replicate our seed set based on diversity characteristics and three spreadsheet that: i) presents the seed set of SLR Original; ii) describes the seed set recommended by tool; and iii) reflects the snowballing process and analysis.</span></strong></p>
Artefatos acerca do artigo Beyond Code: the Development of Soft Skills through Training in Software Engineering
Open the record for dataset details and reuse information.
Comparison of Software Engineering Experiment Quality Evaluation Approaches - Experimental Package
<p>Experimental Package of the Comparison of Experiment Quality Evaluation Approaches</p>
Datasets of "FraSSD: a framework to help teachers in the challenge of developing hard and soft skills in Software Engineering students"
<p>Hard and soft skills are essential for software professionals to perform their daily activities successfully, considering the specificities of the area, which requires a combination of different skills during the software development cycle. Thus, professionals must develop such skills from undergraduate onwards. In this context, although there are related works in the literature, no results were found that were aimed at supporting teachers in developing students' soft skills in the classroom through active education methodologies, focusing on student motivation. Therefore, to provide a model to help teachers in the challenge of contemplating hard and soft skills in a more meaningful teaching-learning process for students enrolled in Software Engineering subjects, this paper presents a framework based on the active methodologies PBL - Project Based Learning and role-play. Emphasis was placed on the six main soft skills required of software engineers: communication, teamwork, organization, leadership, learning, and creativity. To complement the motivational elements, the framework, called FraSSD – Framework for Soft Skills Development, uses some gamification elements, namely competition, scoring, and scoreboard. Finally, the activities established for students are based on Bloom's Taxonomy, which provides progressive knowledge development. As a proof of concept, FraSSD was applied in two Software Engineering classes, and a self-evaluation was conducted on the part of the students before and after the development of the project proposed by the framework, in which students evaluated their level of development in each of the six soft skills worked on during the course. The results were compared through statistical testing, showing an increase in the level of development of soft skills worked on, emphasizing organization, creativity, and teamwork in Class 1 and teamwork, creativity, and leadership in Class 2. Furthermore, a phenomenological analysis of an open question in the self-evaluation enabled FraSSD's positive points to be observed, as well as suggestions for improvements. From the teachers' point of view who applied the framework, although they suggested some modifications, they considered the FraSSD model easy to implement, which helped motivate students as opposed to traditional teaching methods.</p>
Mars Meets Jupyter: Navigating the Interdisciplinary Divide Between Software Engineers and Domain Experts (Appendix)
Open the record for dataset details and reuse information.
Classifying Open-Source Pre-Trained Models and Datasets for Software Engineering
Open the record for dataset details and reuse information.
Conceptual Model for Supporting the Teaching of Software Engineering Controlled Experiments and Quasi-Experiments
<p>Conceptual Model for Supporting the Teaching of Software Engineering Controlled Experiments and Quasi-Experiments</p>
Natural Language Inference Dataset for Software Engineering
<p>Active research in requirements engineering and software engineering necessitates the application of Natural Language Processing (NLP) techniques to address unique challenges and enhance software quality. However, there is a dearth of effective Natural Language Inference (NLI) datasets for training neural network models to generate distributed sentence representations and tackle diverse NLP tasks. In this paper, we present a NLI dataset, tailored specifically to software engineering, empowers neural network models to effectively handle NLP tasks in this domain. The creation of this dataset involved meticulous annotation and careful consideration of diverse sources, including software documentation, user guides, App reviews and different articles related to software systems. Our dataset maintains compatibility with existing NLI datasets like Stanford Natural Language Inference, facilitating seamless adaptation of models without additional preprocessing.</p>
Student exercise reports (in a software engineering course)
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.