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20 results for “software feature”
Appendices of the work "On the perceived relevance of critical internal quality attributes when evolving software features"
<p>Several refactorings performed while evolving software features aim to improve internal quality attributes like cohesion and complexity. Studies show that non-assisted refactorings might worsen, not improve, internal attributes. Current knowledge is scarce on how developers perceive the relevance of critical internal attributes while evolving features. Internal attributes are critical if their measurement assumes anomalous values. This qualitative study investigates the developer's perception on the relevance of critical internal attributes when evolving features. We target six class-level critical attributes: low cohesion, high complexity, high coupling, large hierarchy depth, large hierarchy breadth, and large size. We performed two industry case studies based on online focus group sessions. Developers discussed how much (and why) critical attributes are relevant for adding or enhancing features. We assessed the relevance of critical attributes individually and relatively, reasons behind the relevance of each critical attribute, and interrelations of critical attributes. Low cohesion and high complexity were perceived as very relevant because they often make evolving features hard while tracking failures and adding features. The other critical attributes were perceived as less relevant when reusing code or adopting design patterns. An example of perceived interrelation is high complexity leading to high coupling.</p>
Appendices of the work "On the perceived relevance of critical internal quality attributes when evolving software features"
<p>Several refactorings performed while evolving software features aim to improve internal quality attributes like cohesion and complexity. Studies show that non-assisted refactorings might worsen, not improve, internal attributes. Current knowledge is scarce on how developers perceive the relevance of critical internal attributes while evolving features. Internal attributes are critical if their measurement assumes anomalous values. This qualitative study investigates the developer's perception on the relevance of critical internal attributes when evolving features. We target six class-level critical attributes: low cohesion, high complexity, high coupling, large hierarchy depth, large hierarchy breadth, and large size. We performed two industry case studies based on online focus group sessions. Developers discussed how much (and why) critical attributes are relevant for adding or enhancing features. We assessed the relevance of critical attributes individually and relatively, reasons behind the relevance of each critical attribute, and interrelations of critical attributes. Low cohesion and high complexity were perceived as very relevant because they often make evolving features hard while tracking failures and adding features. The other critical attributes were perceived as less relevant when reusing code or adopting design patterns. An example of perceived interrelation is high complexity leading to high coupling.</p>
Architectural Feature Re-Modularization for Software Product Line Evolution
<p>Extensive maintenance leads to the Software Product Line Architecture<br> (PLA) degradation over time. When there is the need of<br> evolving the Software Product Line (SPL) to include new features,<br> or move to a new platform, a degraded PLA requires considerable<br> effort to understand and modify, demanding expensive refactoring<br> activity. In the state of the art, search-based algorithms are used to<br> improve PLA at package level. However, recent studies have shown<br> that the most variability and implementation details of an SPL are<br> described in the level of classes. There is a gap between existing<br> approaches and existing practical needs. In this work, we extend<br> the current state of the art to deal with feature modularization in<br> the level of classes by introducing a new search operator and a set<br> of objective functions to deal with feature modularization in a finer<br> granularity of the architectural elements, namely at class level. We<br> evaluated the proposal in an exploratory study with a PLA widely<br> investigated and a real-world PLA. The results of quantitative and<br> qualitative analysis point out that our proposal provides solutions<br> to properly re-modularize features in a PLA, being preferred by<br> practitioners, in order to support the evolution of SPLs.</p>
A Hybrid Feature Location Technique for Re-engineering Single Systems into Software Product Lines
<p>The dataset used for evaluating the hybrid feature location technique presented in the paper: "A Hybrid Feature Location Technique for Re-engineering Single Systems into Software Product Lines". This enables reproducibility, evaluation, and comparison of our study.</p> <p>_________________________________________________________________________________________________________</p> <p>Folder "Dataset" contains for each subject system used:</p> <p>(i) the artificial variants and their configurations;</p> <p>(ii) the ECCO repository containing the traces;</p> <p>(iii) the ground truth and composed variants;</p> <p>(iv) the metrics results.</p> <p>_________________________________________________________________________________________________________</p> <p>Folder "Scenarios" contains for each subject system used:</p> <p>(i) the videos recorded from exercising features on GUI.</p>
Appendices of the work "On the perceived relevance of critical internal quality attributes when evolving software features"
<p><strong>Context:</strong> Several refactorings performed while evolving software features aim to improve internal quality attributes like cohesion and complexity. Studies shows that non-assisted refactorings might worsen, not improve, internal attributes. Current knowledge is scarce on how developers perceive the relevance of critical internal attributes while evolving features. Internal attributes are critical if their measurement assumes anomalous values. <strong>Objective:</strong> This qualitative study aims at revealing the developer's perception on the relevance of critical internal attributes when evolving features. We target six class-level critical attributes: low cohesion, high complexity, high coupling, large hierarchy depth, large hierarchy breadth, and large size. <strong>Method:</strong> We performed two industry case studies based on online focus group sessions. We asked developers to discuss how much (and why) critical attributes are relevant for adding or enhancing features. We assessed the relevance of critical attributes individually and relatively, reasons behind the relevance of each critical attribute, and interrelations of critical attributes. <strong>Results:</strong> Low cohesion and high complexity were perceived as very relevant because they often make evolving features hard while tracking failures and adding features. The other critical attributes were perceived as less relevant when reusing code or adopting design patterns, for instance. Examples of interrelations include large size leads to low cohesion and high complexity leads to high coupling. <strong>Conclusions:</strong> Our findings could be combined with previous results on how refactorings affect quality attributes to assist developers in applying refactorings that may have a practically relevant impact on critical attributes.</p>
SLDeep: Statement-Level Software Defect Prediction Using Deep-Learning Models on Static Code Features
<p>Software defect prediction (SDP) seeks to estimate fault-prone areas of the code to focus testing activities on more suspicious portions. Consequently, high-quality software is released with less time and effort. The current SDP techniques however work at coarse-grained units, such as a module or a class, putting some burden on the developers to locate the fault. To address this issue, we propose Statement-Level software defect prediction using Deep-learning model (SLDeep). To reify our proposal, we defined a suite of 32 statement-level metrics, such as the number of binary and unary operators used in a statement. Then, we applied as learning model, long short-term memory (LSTM). The significance of SLDeep for intelligent and expert systems is that it demonstrates a novel use of deep-learning models to the solution of a practical problem faced by software developers. We conducted experiments using more than 100,000 C/C++ programs within the Code4Bench. The programs total 2,356,458 lines of code with 292,064 faulty lines. The benchmark comprises diverse set of programs and versions, written by thousands of developers. Therefore, it tends to give a model that can be used for cross-project SDP. In the experiments, our trained model could successfully classify the unseen data with average performance measures 0.945, 0.971, and 0.976 in terms of recall, precision, and accuracy, respectively. These experimental results suggest that SLDeep is effective for statement-level SDP. The impact of this work is twofold. Working at statement-level further alleviates developer’s burden in pinpointing the fault locations. Second, cross-project feature of SLDeep helps defect prediction research become more industrially-viable</p> <p>for more information visit <a href="https://github.com/sldeep/SLDeep">https://github.com/sldeep/SLDeep</a></p>
Adaptations of Scrum roles in software projects: Survey and Representation Tentative with Feature Models
<p>Vídeo short paper sobre adapatações dos papéis do Scrum</p>
The Life Cycle of Features in Highly-Configurable Software Systems Evolving in Space and Time
<p>Dataset covering the entire development history of four open-source systems from different domains, covering a total of 37500 commits from up to 20 years of development, which can serve as a source of information for new studies on the evolution of systems in space and time.</p>
Grip on Software sprint features
<p>Data set of attributes and metrics that describe events during Scrum sprints at two software development organizations, both governmental IT organizations based in the Netherlands.</p>
Leveraging Relational Concept Analysis for Automated Feature Location in Software Product Lines - Artefacts DataSet
<p>This Archive contains the Artefact of the paper, submitted at GPCE2021 :<br> <a href="https://doi.org/10.1145/3486609.3487208">Leveraging Relational Concept Analysis for Automated Feature Location in Software Product Lines</a></p> <p> </p> <p>It contains the dataset and the results of our Feature Location techniques when applied to this dataset.</p>
Data from: PopART: full-feature software for haplotype network construction
1. Haplotype networks are an intuitive method for visualising relationships between individual genotypes at the population level. 2. Here, we present popart, an integrated software package that provides a comprehensive implementation of haplotype network methods, phylogeographic visualisation tools and standard statistical tests, together with publication-ready figure production. 3. popart also provides a platform for the implementation and distribution of new network-based methods – we describe one such new method, integer neighbour-joining. 4. The software is open source and freely available for all major operating systems.
User Personas Improve Social Sustainability by Encouraging Software Developers to Deprioritize Antisocial Features
<p>This dataset contains the supplementary material for the paper "User Personas Improve Social Sustainability by Encouraging Software Developers to Deprioritize Antisocial Features".</p> <p>The dataset includes:</p> <ul> <li>Pilot study material and analysis document</li> <li>Task Materials(Instruction, prioritization worksheet, personas, stakeholder map)</li> <li>Instructions for Invigilators (experimental protocol)</li> <li>Consent form (de-identified)</li> <li>Data sheet</li> <li>Preliminary data analysis script and visualizations - Python Notebook</li> <li>Cummulative Link Mixed Model analysis script - R</li> <li>Propotional Odds analysis script - R</li> <li>Instructional videos<br><br>***This version includes written experimental protocol for invigilators, instructional videos for participants and Propotional Odds assumption analysis script.</li> </ul>
Supplementary materials for "Testing Software's Changing Features with Environment-Driven Abstraction Identification"
<p><strong>Supplementary materials of the paper entitled:</strong></p> <p><strong>“Testing Software’s Changing Features with Environment-Driven Abstraction Identification”</strong></p>
Dataset used for paper "Issues-Driven Features for Software Fault Prediction"
<p> </p> <div>Dataset used for paper "Issues-Driven Features for Software Fault Prediction".</div> <div> </div> <div> </div> <div> </div> <div>The dataset contains 86 projects from the open source organizations Apache and Spring were written in Java that managed their source code using the Git version control system and an issue tracking system (JIRA or BUGZILLA). </div> <div> </div> <div>For each project, we extracted data for software fault prediction (SFL) task as follows:</div> <div> </div> <div>First, we filtered out projects without reported resolved bugs or less than 5 released versions.<br> Then we iterated the resolved bugs and mapped them to the commits that fixed them.<br> Next, for each version, we labeled the faulty files in the version. A faulty file is a file that was modified in a commit in the version that resolved a bug. <br> The methodology for labeling the files is a variant of the very known approach implemented in the {\it SZZ} algorithm, which accounts for its vulnerabilities.</div> <div>Finally, for each project, we filtered out versions with faulty files' ratios lower than 5\% and higher than 30\%. The remaining set includes a good representation of bugs and reduces the class imbalance produced by the low number of defects. In addition, filtering versions with a low number of bugs helps to prune outliers, such as a version created to fix specific issues.</div> <div> </div> <div>Extracted using Beirut repository mining tool:</div> <p>https://github.com/beirut-repository-mining/repository_mining</p> <p> </p>
Data from: PopART: full-feature software for haplotype network construction
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Adaptations of Scrum roles in software projects: Survey and Representation Tentative with Feature Models
<p>Vídeo sobre adaptações dos papéis do Scrum</p>
Adaptations of Scrum roles in software projects: Survey and representation tentative with feature models
<p>Vídeo em português</p> <p> </p> <p>This work is supported by CAPES/Brazil (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) code 001.</p>
Architectural Feature Re-Modularization for Software Product Line Evolution
<p>Extensive maintenance leads to the Software Product Line Architecture<br> (PLA) degradation over time. When there is the need of<br> evolving the Software Product Line (SPL) to include new features,<br> or move to a new platform, a degraded PLA requires considerable<br> effort to understand and modify, demanding expensive refactoring<br> activity. In the state of the art, search-based algorithms are used to<br> improve PLA at package level. However, recent studies have shown<br> that the most variability and implementation details of an SPL are<br> described in the level of classes. There is a gap between existing<br> approaches and existing practical needs. In this work, we extend<br> the current state of the art to deal with feature modularization in<br> the level of classes by introducing a new search operator and a set<br> of objective functions to deal with feature modularization in a finer<br> granularity of the architectural elements, namely at class level. We<br> evaluated the proposal in an exploratory study with a PLA widely<br> investigated and a real-world PLA. The results of quantitative and<br> qualitative analysis point out that our proposal provides solutions<br> to properly re-modularize features in a PLA, being preferred by<br> practitioners, in order to support the evolution of SPLs.</p>
Materials for Publication: Software Feature Request Detection in Issue Tracking Systems
<p>Additional figures, tables, experimant data, code, and results.</p> <p>See README.md for more information.</p>
Adaptations of Scrum roles in software projects: Survey and Representation Tentative with Feature Models
<p>Vídeo do short paper sobre papéis do Scrum</p>
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)
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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.