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192 results for “Software Enginering”
Can a Chatbot Support Software Engineers with Load Testing? Approach and Experiences
<p>Supplementary material for the paper:</p> <p>Dusan Okanovic, Samuel Beck, Lasse Merz, Christoph Zorn, Leonel Merino, Andre van Hoorn, and Fabian Beck. Can a chatbot support software engineers with load testing? Approach and experiences. In Proceedings of the 11th ACM/SPEC International Conference on Performance Engineering (ICPE 2020), 2020. <a href="https://dx.doi.org/10.1145/3358960.3375792">https://dx.doi.org/10.1145/3358960.3375792</a></p>
The Lack of Shared Understanding of Non-Functional Requirements in Continuous Software Engineering: Accidental or Essential?
<p>Study Information</p> <p>We conducted a case study of three small organizations scaling up continuous software engineering to further understand and identify factors that contribute to lack of shared understanding of non-functional requirements (NFRs), and its relationship to rework. To conceptualize lack of shared understanding of NFRs we traced it to rework development tasks. Seeking to shed light on the complex relationship between shared understanding, rework, and CSE, our study examined forty-one NFR-related development tasks identified as rework and was driven by the following research questions:</p> <ol> <li>What contributes to lack of shared understanding of NFRs?</li> <li>Which NFRs are most associated with a lack of shared understanding?</li> <li>What amount of a lack of shared understanding of NFRs is accidental versus essential?</li> </ol> <p>To answer our research questions, we conducted a multi-case study using a mixed-methods approach in collaboration with three independent organizations using qualitative and immersive techniques. For this study our organizations are referred to as Alpha, Beta, and Gamma. We performed a preliminary study of each organization to build a context for each organization. We then collected data from project task management repositories and analyzed the tasks to uncover 41 NFR-related software development tasks as rework due to a lack of shared understanding of NFRs.</p> <p>Data Analysis</p> <p>We performed qualitative analysis through focus groups at each organization. We used an open-coding [1] approach to develop a purely inductive codebook, which minimizes a coder's ability to force a bias of any particular hypothesis. In the initial coding phase, a transcript was independently coded by two coders, after which an agreement session was held to discuss the codes, consolidate the codebook, and to calculate Cohen's kappa coefficient (using sklearn.metrics cohen_kappa_score). We continued coding in pairs until our inter-rater reliability met substantial agreement. After which we individually coded the remaining transcripts followed by an expert rater reviewer.</p> <p>This data represents two artifacts from our research in evaluating the complex relationship between shared understanding, non-functional requirements, continuous software engineering, and rework. It includes the resulting thematic synthesis codebook and inter-rater kappa values.</p> <p>Artifact Descriptions</p> <p>Our replication package contains two artifacts:<br> 1. Codebook.csv: The codebook itself contains a row for each code used (48 in total), including the code name, a brief description of the code, which round of coding that code was introduced, the total number of tasks that code appeared in (at least once), the number of tasks that code appeared in for each organization (Alpha, Beta, and Gamma), the total number of occurrences across all tasks, the number of occurrences across across each organization (Alpha, Beta, and Gamma), and the number of occurrences for each task.</p> <p>For example, the code 'BusinessContext' was used when 'Talking about information from the business side of the organization' and appeared in the first transcript we coded (1). The 'BusinessContext' code appeared in 19/41 tasks (10 at Alpha, 8 at Beta, and 1 at Gamma). Furthermore, the 'BusinessContext' code was used a total of 72 times (43 at Alpha, 22 at Beta, and 7 at Gamma). The remaining 41 columns represent the number of occurrences for each task, e.g. it appeared 3 times in task A-2 (Alpha's second task).</p> <p>2. kappa-values.csv: Contains the interview rounds (in order of coding) and the associated kappa values calculated for our inter-rater coding agreement.</p> <p>Usefulness</p> <p>While we recognize that the value and usefulness of our replication package is yet to-be-determined, in the interest of transparency of open science we published our artifacts. In light of this, we hope that these artifacts are useful to either replicate our findings or to further analyze to produce other enlightening results.</p> <p>References</p> <ol> <li>J. M. Corbin and A. Strauss, “Grounded theory research: Procedures, canons, and evaluative criteria,”Qualitative sociology, vol. 13, no. 1,pp. 3–21, 1990.</li> </ol>
A Comparison of Natural Language Understanding Platforms for Chatbots in Software Engineering
<p>The file contains the training dataset used in our study. And, it includes the results of all NLUs for both tasks.</p>
Acceptance Behavior Theories and Models in Software Engineering
<p><strong>Electronic supplement for a mapping study on acceptance behavior theories and models in SE</strong></p> <p>Please refer to the following paper when you cite/use this data:</p> <p>Jürgen Börstler, Nauman bin Ali, Kai Petersen, Emelie Engström (2024).<br>Acceptance behavior theories and models in software engineering — A mapping study.<br><em>Information and Software Technology</em>.<br>https://doi.org/10.1016/j.infsof.2024.107469</p> <p><strong>Overview of the electronic supplement</strong></p> <ol> <li>An overview-file (README.docx) comprising the following: <ol> <li>An overview of the supplements.</li> <li>A plain list of the theories and models of acceptance behavior used for constructing the search string.</li> <li>A plain list of the software engineering venues used for constructing the search string.</li> <li>A ready-to-use search string for Scopus (plain text).</li> <li>A plain list of the 47 included primary studies.</li> </ol> </li> <li>A separate Bibtex-file (p1-p47.bib) with all 47 included primary studies.</li> <li>A separate Excel-file (data extraction.xlsx) comprising the following sheets: <ol> <li>The data extracted for the 47 included primary studies.</li> <li>The 27 primary studies excluded during full-text reading and data extraction.</li> </ol> </li> </ol>
Heidelberg Engineering ANTERION Software Comparison Study
ClinicalTrials.gov study NCT06657716. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Heidelberg Engineering ANTERION Software Comparison Precision and Agreement Study
ClinicalTrials.gov study NCT06397976. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Comparison of Natural Language Understanding Platforms for Chatbots in Software Engineering
<p>The used dataset for training the NLUs and the results of each NLU from both tasks.</p>
A Comparison of Natural Language Understanding Platforms for Chatbots in Software Engineering
<p>The dataset used to train the NLUs, and the results of each NLU from both tasks.</p> <p> </p>
Flipped Classroom in Software Engineering: A Systematic Mapping Study
<p>Vídeo (backup) para apresentação de contingência</p>
Raw Data for Empirical Software Engineering
<p>The ZIP archive contains the raw data of measurement results for each guideline.</p>
Interview Study About Coursework Consultation Sessions with GitLab Metrics in a Software Engineering Course
<p>This data repository is created to share materials used in an interview study about coursework consultation sessions with GitLab Metrics in a software engineering course, and selected quotations with their corresponding themes.</p>
Classifying Open-Source Pre-Trained Models and Datasets for Software Engineering
<p>The replication package for the short paper titled 'Classifying Open-Source Pre-Trained Models and Datasets for Software Engineering' is provided. It includes a README file and accompanying scripts with comprehensive instructions to facilitate the replication of the analysis presented in the paper.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.