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192 results for “software engineering”
Proactive Conflict Detection for Collaborative Model-driven Software Engineering (Evaluation Data)
<p>Results of the evaluation for the paper "Proactive Conflict Detection for Collaborative Model-driven Software Engineering"</p>
Value-based Software Engineering: A Systematic Mapping Study
<p><strong>Abstract</strong><br> <strong>Background: </strong>Integrating value-oriented perspectives into the principles and practices of software engineering is fundamental to ensure that software development activities address key stakeholders’ views and also balance short-and long-term goals. This is put forward in the discipline of value-based software engineering (VBSE)<br> <strong>Aim:</strong> This study aims to provide an overview of VBSE with respect to the research efforts that have been put into VBSE.<br> <strong>Method:</strong> We conducted a systematic mapping study to classify evidence on value definitions, studies’ quality, VBSE principles and practices, research topics, methods, types, contribution facets, and publication venues.<br> <strong>Results:</strong> From 143 studies we found that the term “value” has not been clearly defined in many studies. VB Requirements Engineering and VB Planning and Control were the two principles mostly investigated, whereas VB Risk Management and VB People Management were the least researched. Most studies showed very good reporting and relevance quality, acceptable credibility, but poor in rigor. The main research topic was Software Requirements and case study research was the method used the most. The majority of studies contribute toward methods and processes, while very few studies have proposed metrics and tools.<br> <strong>Conclusion:</strong> We highlighted the research gaps and implications for research and practice to support VBSE.</p>
Using Open Citation Databases for Snowballing in Software Engineering Research
<p>Dataset for our study on the coverage of software engineering articles in open citation databases:</p> <ul> <li>a list of the 23 sampled venues with their respective CORE ranks and publishers, <ul> <li>01-venues.csv,</li> </ul> </li> <li>a list of the 204 sampled articles with their respective number of references/citations per citation database, <ul> <li>02-articles.csv (articles with publication information),</li> <li>03-references-absolute.csv (number of references in published PDF & absolute numbers for reference coverage in databases),</li> <li>04-references-relative.csv (relative numbers for reference coverage in databases),</li> <li>05-citations-absolute.csv (absolute numbers for citation coverage in databases),</li> <li>06-citations relative.csv (relative numbers for citation coverage in databases),</li> </ul> </li> <li>a list of the 8 articles analyzed in more detail with complete references data from the citation databases, <ul> <li>07-selected-articles.csv (articles with publication information),</li> <li>08A–08H (comparison of references found in databases for each article),</li> </ul> </li> <li>and additional statistical measures and plots <ul> <li>09-Statistics.{pdf,xlsx} (statistical measures – i.e., minimum, maximum, median, average, variance – for the whole dataset and for subsets by publisher, CORE rank, or year of publication),</li> <li>10-Figures.zip (figures for references as shown in the study and additional figures for citations – each in EPS and PNG format).</li> </ul> </li> </ul>
A Conceptual Model to Support Teaching of Software Engineering Controlled (Quasi-)Experiments - Evaluation of the Concept Model
<p>A Conceptual Model to Support Teaching of Software Engineering Controlled (Quasi-)Experiments - Evaluation of the Concept Model</p>
Exploring Psychological Safety in Software Engineering
<p>The database used in the article entitled "Exploring Psychological Safety in Software Engineering: Insights from Stack Exchange" consists of two files: "All Results" and "Database Sbes." The "All Results" file includes all search results, while the "Database Sbes" file contains the categorization of selected data.</p>
Dataset: Challenges, Strengths, and Strategies of People with ADHD in Software Engineering: A Case Study
<p>This dataset accompanies the paper "Challenges, Strengths, and Strategies of People with ADHD in Software Engineering: A Case Study". It contains interview guides as well as anonymous interview transcripts for six interviewees who explicitly consented to the publication.</p> <p>Specifically:</p> <ul> <li>Interview transcripts are Excel files starting with "<strong>interview</strong>".</li> <li>The interview instruments used for the 19 interviews are named "<strong>interview_guide_professionals</strong>" followed by a version number. V1 was used for the first 3 interviews, V2 for the 9 following interviews, and V3 for the remaining 7.</li> <li><strong>manager_feedback_guide.pdf</strong> is the interview guide used for discussions with the 4 managers. The preliminary results we showed them are depicted in <strong>01_challenges_themes_relations_noStrat_v2</strong>, <strong>02_strengths_themes_relations_v1</strong> and <strong>02_strengths_themes_relations_v2</strong></li> <li><strong>consent_form.pdf</strong> is a PDF version of the online form used to handle consent and intake of interviewees.</li> </ul>
Achieving Energy Efficiency with a Software Product Line Engineering Approach
<p>This is a Live PhD. Thesis Presentation; Also playable at:</p><p><a href="https://youtu.be/yaYEoj4S3pc">https://youtu.be/yaYEoj4S3pc</a></p><p>Please access and cite the published PhD. Thesis book: <a href="https://doi.org/10.5281/zenodo.10007921 ">https://doi.org/10.5281/zenodo.10007921 </a></p><ul><li><strong>Author</strong>: Daniel-Jesus Munoz</li><li><strong>Directors</strong>: Lidia Fuentes and Monica Pinto.</li></ul><p>CAOSD group, Universidad de Málaga, Andalucía Tech, Spain </p><p>Post-defense presentation recorded at Universität Ulm in 2023.</p><p>Energy-aware software design can energy-aware software design can reduce total energy consumption by 30-90%. However, the different ways of measuring energy consumption in real time are very complex. Energy readings are provided as the total energy consumption in joules or the rate of energy consumption in watts. For battery-powered battery-powered devices, joules per task is a more interesting metric, while watts per task is more commonly used for battery-powered devices. per task is more used for devices directly connected to power. The usual approach to modelling and storing approach to modelling and storing power consumption readings is to describe them as a characteristic of individual components, such as monetary cost. individual components, such as the monetary cost of a hardware component. However, the energy consumption values have many interactions between components, which makes it difficult to describe them with individual, static energy values. This makes it difficult to describe them in terms of individual, static energy values. Instead, we can store and energy information can be stored and populated in collaborative databases. The IEA and Datarade offer free databases with energy consumption data. free databases with energy consumption data for energy efficiency and sustainability analysis. Without However, the databases are not scalable for highly configurable systems because of the curse of the dimension. Our work focuses on Industry 4.0, specifically on Cyber-Physical Systems (acronym CPS), which are characterised by their high configurability and adaptability, presenting a large number of alternatives and a colossal number of alternatives and a colossal number of different systems in operation. This is known as the This is known as the search/solution space, the size of which is the set of all possible points that satisfy an optimisation problem. optimisation problem. Partially known solution spaces are a common problem in many fields, such as computer engineering, machine learning, artificial intelligence and goal-oriented optimisation. engineering, machine learning, artificial intelligence and goal-oriented optimisation. The Constraint Satisfaction Problems (CSP) are mathematical problems defined as a set of objects whose state must satisfy a set of constraints. set of objects whose state must satisfy a set of constraints. Variability Models (acronym VMs) are tree-like structures used to represent the commonalities and differences of a CPS. Numerical Features (NFs) can be used in VMs to represent quantitative properties of the system, but they can be used to represent quantitative properties of the system, but most tools do not support NFs. In addition, NFs increase the size of the VM, NFs increase the size of the solution space by multiplying it by its domain size, which makes large solution spaces colossal. large solution spaces into colossal ones. Quality Models (QMs) are tree structures that are used to determine which Quality Attributes (QAs) such as energy efficiency are to be taken into account when evaluating a project. which Quality Attributes (QAs) such as energy efficiency are to be taken into account when evaluating a system. system. ISO/IEC 25010 is the most popular QM formalisation, which groups QAs into eight different types. The Automated reasoning is the automation of formal logical reasoning to compute different types of information about system models. Examples are providing a VM or QM to a reasoning tool, and calculating the size of the reasoning tool, and calculating the size of the solution space, checking the satisfiability of the model, or generating only optimal systems based on objective functions. only optimal systems based on objective functions based on one or more QAs. This thesis aims to find a native modelling and reasoning approach for a unified Quality and Variability Model (QVM). Variability and Quality Model (QVM). The aim is to develop an approach that supports modelling and reasoning for the reasoning oriented optimisation of numerical characteristics, an algebraic framework for unified QVMs, an online eco-assistant for optimising a user-constrained solution space measured for quality, and an algorithm and an online quality, and an algorithm and a web tool for learning the influences of energy and characteristics of user-constrained, domain-unknown and partially measured solution spaces.</p>
EvalQuiz - LLM-based Automated Generation of Self-Assessment Quizzes in Software Engineering Education
<p>Self-assessment quizzes after lectures, educational videos, or chapters are a commonly used method in software engineering (SE) education to give students the opportunity to test their gained knowledge. However, the creation of these quizzes is time-consuming, cognitively exhausting, and complex, as an expert in the field needs to create the quizzes and review the lecture material for validity. Therefore, this paper presents a concept to automatically generate self-assessment quizzes based on lecture material using a large language model (LLM) to reduce lecturers' workload and simplify the general quiz creation process. The developed prototype was handed to experts, who subsequently evaluated the approach. The results show that automatic quiz generation saves time and the quizzes cover the delivered lecture material well. However, the generated quizzes often lack originality and versatility. Therefore, further prompt engineering might be required to achieve more elaborate results.</p>
Are Game Engines Software Frameworks? A Three-perspective Study
<p>Dataset for the paper: "Are Game Engines Software Frameworks? A Three-perspective Study"</p>
An exploration of the codes of ethics of various organisations that hire software engineers.
<p>A spreadsheet identifying ethical imperatives discovered in the published codes of practice from U.S. technology firms and U.K. universities.</p>
Survey of software engineering in code used in published papers
<p><strong>Background</strong>: Computer code underpins modern science, and at the present time has a crucial role in leading our response to the COVID-19 pandemic. While models are routinely criticised for their assumptions, the algorithms and the quality of code implementing them often avoid scrutiny and, hence, scientific conclusions cannot be rigorously justified.</p> <p><strong>Problem</strong>: Assumptions in programs are hard to scrutinise as they are rarely explicit in published work. In addition, both algorithms and code have bugs, effectively unknown assumptions that have unwanted effects.</p> <p>Code is fallible. Any model interpretation that relies on code is therefore fallible, and if the code is not published with adequate documentation, the code cannot be scrutinised. In turn, the scientific claims cannot be properly scrutinised.</p> <p><strong>Solutions</strong>: Code can be made much more reliable using software engineering good practice. Three specific solutions are proposed. First, professional software engineers can help and should be involved in critical research. Secondly, "Software Engineering Boards" (supplementing and analogous to Ethics or Institutional Review Boards) must be instigated and used. Thirdly, code, when used, must be considered an intrinsic part of any publication, and therefore must be formally reviewed by competent software engineers.</p> <p><em>The paper's Supplementary Material includes a summary of professional software engineering best practice, particularly as applied to scientific research and publication.</em></p>
Experimental Data for: Research Perspective on Supporting Software Engineering via Physical 3D Models
<p>Experimental data for the experiment presented in the technical report 1507: "Research Perspective on Supporting Software Engineering via Physical 3D Models"</p>
The Effects of Education on Students’ Perception of Modeling in Software Engineering
<p>The attached file accompanies the paper titled "The Effects of Education on Students’ Perception of Modeling in Software Engineering". This file contains both the raw data and summary data from the survey conducted at the three institutions, NAU, BGU, and Concordia.</p>
Requirement prioritization in Software Engineering: a systematic literature review update
<div> <div> <div> </div> </div> </div> <div> <div> <div> <div> <div> <div> <p>A data extraction form was developed to collect all relevant information from the identified studies and organize the selection process in this updated systematic literature review. The main information included in this form comprises the protocol, an identifier (ID) for each study, bibliographic references, and answers to the research questions.</p> </div> </div> </div> </div> </div> </div>
Diversity in Software Engineering Conferences and Journals
<p>This repository includes the data used in the research in the article "Diversity in Software Engineering Conferences and Journals" submitted to the Journal of Systems and Software in October 2023. This repository contains .csv and .json files that include the data of all research papers published in the conferences ICSE, ASE, and FSE, along with the journals IEEE TSE and ACM TOSEM from the years 2010-2022 along with the list of names of Programming and Organizing Committee members and Editorial Board members. This data was sourced from DBLP and the conference websites/journal front matter pages. The names of the authors and committee members have been processed using NamSor to obtain the corresponding ethnicity (using the "Name US Race" feature) and gender (using the "Genderize name from first name and last name" feature). The affiliated institution and corresponding country for each first author were determined using the Scopus database.</p>
Running a Red Light: An Investigation into Why Software Engineers (Occasionally) Ignore Coverage Checks --- Appendix
<p>This online appendix contains the anonymised responses to our survey, which we have analysed in our report. In addition, it also includes the mapping of axial coding codes to larger groups, and (if not self-evident) a further explanation of these groups.</p>
Dataset for "Beyond Self-Promotion: How Software Engineering Research Is Discussed on LinkedIn"
<p>This repository contains the artifacts of our study on how software engineering research papers are shared and interacted with on LinkedIn, a professional social network. This includes:</p> <ul> <li><em>included-papers.csv</em>: the list of the 79 ICSE and FSE papers we found on LinkedIn</li> <li><em>linkedin-post-data.csv</em>: the final data of the 98 LinkedIn posts we collected and synthesized</li> <li><em>linkedin-post-scraping.zip</em>: the scripts used to automatically collect several attributes of the LinkedIn posts</li> <li><em>analysis.zip</em>: the Jupyter notebook used to analyze and visualize <em>linkedin-post-data.csv</em></li> </ul>
Harmonising Contributions: Exploring Diversity in Software Engineering through CQA Mining on Stack Overflow
<p>Community question-and-answering platforms dedicated to software engineering, such as Stack Overflow, have assumed indispensable roles in fostering a thriving global knowledge ecosystem. As these platforms suffer from diversity-related issues, investigating the underlying reasons behind such challenges becomes imperative to devise potential intervention strategies.</p> <p>The proposed study highlights Stack Overflow users’ contribution profiles, both in isolation and relative to various diversity metrics, including GDP and access to electricity. Finally, the study explores whether these contribution profiles extend to the city and state levels.</p> <p>This replication package complements our study, prompting future scholars to further examine our research process or conduct follow up analyses.</p>
Prospects for Quantum Software Engineering in the Next Decade - Supplementary material
<p>This supplementary material corresponds to the study "<em>Prospects for Quantum Software Engineering in the Next Decade</em>" for the Software Engineering in 2030 Workshop (SE2030).</p> <p>In this supplementary material you will find the papers found in the search for the terms "<em>Quantum Software Engineering</em>" in the bibliographic databases of Scopus and Google Scholar, as well as their evolution since 2004.</p>
Replication Package: Pandemic Startup Software Engineering: An Experience Report on the Development of a COVID-19 Certificate Verification System
<p><strong>Welcome to the public repository for the additional content of the paper "Pandemic Startup Software Engineering: An Experience Report on the Development of a COVID-19 Certificate Verification System" (Journal of Systems and Software)<br></strong></p> <p>This repository provides additional information to the experience report, including the following files:</p> <ul> <li>survey_questions_de.txt: sheet containing the online questionnaire in German (original language)</li> <li>survey_questions_en.txt: sheet containing the online questionnaire translated into English</li> <li>survey_answers_original.csv: sheet containing the extracted questionnaire data of the participants in German (original language)</li> <li>survey_analysis.csv: sheet containing the analysis of the extracted questionnaire data in English</li> </ul>
ScienceDex guides
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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.