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14 results for “non-functional requirement”
The Role of Informal Communication in Building Shared Understanding of Non-Functional Requirements in Remote Continuous Software Engineering
<p><strong>Study Information</strong></p> <p>We conducted an ethnography-informed case study of a remote software organization that adopts CSE practices to explore how the organization builds a shared understanding of NFRs. Our study uses semi-structured interviews with a period of observations to answer the following research questions:</p> <p> </p> <ol> <li> <p>How does a remote software organization that adopts CSE practices reach a shared understanding of NFRs?</p> </li> <li> <p>What are the limitations to the shared understanding of NFRs in a remote software organization that adopts CSE practices?</p> </li> <li> <p>What organizational practices for remote collaboration supported a shared understanding of NFRs?</p> </li> </ol> <p> </p> <p>In our study, we refer to our partner organization as Alpha. We used ethnography-informed methods to study Alpha's practices and processes and how they approach a shared understanding of NFRs in their product development. </p> <p> </p> <p><strong>Data Analysis</strong></p> <p>We performed a qualitative study through semi-structured interviews and observations. We use the open, axial and selective coding approach from grounded theory [1] to create our codebook, which informed the results and discussion of our study. Two independent coders held agreement sessions to discuss the codes, consolidate the codes and calculate the inter-rater reliability using the Cohen Kappa's coefficient for measuring observer agreement for categorical data [2]. </p> <p> </p> <p><strong>Artifact Descriptions</strong></p> <p>Our replication package contains three artifacts:</p> <p>1. Codebook.csv: The codebook contains rows for the list of codes used, including the code name and the description of the codes. The codes are the final set of themes derived during the thematic analysis of the interview responses. For example, 'Gaps in communication' means when interview participants describe miscommunications due to team members making assumptions about a project/process or having unclear expectations for a project.</p> <p>2. kappa-scores.csv: This contains the associated kappa values for each round of inter-rater agreement sessions. For each agreement session, the Cohen Kappa's coefficient was calculated from the number of agreements and disagreements of codes within one or two interview transcripts. The Kappa values represent the level of agreement ranging from 0 to 1, where > 0.6 represents substantial agreement. </p> <p>3. Interview-questions.csv: This contains the interview questions used in the semi-structured interviews. Some of the interview questions varied depending on the interviewee’s role, experience and the flow of the interviews.</p> <p><strong> </strong></p> <p><strong>Usefulness</strong></p> <p>We recognize that the value and usefulness of our replication package are yet-to-be-determined. In the interest of transparency of open science, we published our artifacts. We hope that these artifacts are useful to either replicate our findings or to further analyze them to produce other enlightening results.</p> <p><strong> </strong></p> <p><strong>References</strong></p> <p>1. Rashina Hoda, James Noble, and Stuart Marshall. "Grounded theory for geeks". In: Proceedings of the 18th conference on pattern languages of programs. 2011, pp. 1–17.</p> <p>2. J Richard Landis and Gary G Koch. "The measurement of observer agreement for categorical data". In: biometrics (1977), pp. 159–174.</p> <p><strong> </strong></p> <p> </p>
Category Theory Framework for Variability Models with Non-functional Requirements @ CAiSE 21
<p><strong>Your can watch this video in my Youtube channel:</strong></p> <p><strong><a href="https://youtu.be/rX50Q3fpMZE">https://youtu.be/rX50Q3fpMZE</a></strong></p> <p><strong>This is a Live Conference Presentation, please access and cite the published version of the respective publication:</strong></p> <p><strong><a href="https://doi.org/10.1007/978-3-030-79382-1_24">https://doi.org/10.1007/978-3-030-79382-1_24</a></strong></p> <p>In Software Product Line (SPL) engineering one uses Variability Models (VMs) as input to automated reasoners to generate optimal products according to certain Quality Attributes (QAs). Variability models, however, and more specifically those including numerical features (i.e., NVMs), do not natively support QAs, and consequently, neither do automated reasoners commonly used for variability resolution. However, those satisfiability and optimisation problems have been covered and refined in other relational models such as databases. Category Theory (CT) is an abstract mathematical theory typically used to capture the common aspects of seemingly dissimilar algebraic structures. We propose a unified relational modelling framework subsuming the structured objects of VMs and QAs and their relationships into algebraic categories. This abstraction allows a combination of automated reasoners over different domains to analyse SPLs. The solutions’ optimisation can now be natively performed by a combination of automated theorem proving, hashing, balanced-trees and chasing algorithms. We validate this approach by means of the edge computing SPL tool HADAS.</p>
Data augmentation for Multi-Classification of Non-Functional Requirements - Dataset
<p>There are four datasets:</p> <p>1.Dataset_structure indicates the structure of the datasets, such as column name, type, and value.</p> <p>2. Spanish_promise_exp_nfr_train and Spanish_promise_exp_nfr_test are the non-functional requirements of the Promise_exp[1] dataset translated into the Spanish language.</p> <p>3. Balanced_promise_exp_nfr_train is the new balanced dataset of Spanish_promise_exp_nfr_train, in which the Data Augmentation technique with chatGPT was applied to increase the requirements with little data and random undersampling was used to eliminate requirements.</p> <p>The labeling schema, similar to PROMISE NFR, includes the following categories: A: Availability, PO: Portability, L: Legal, FT: Fault tolerance, SC: Scalability, MN: Maintainability, LF: Look and feel, PE: Performance, O: Operational. US: Usability, and SE: Security.</p>
Annotation-based Modeling of Non-functional Requirements and Analysis Results in Domain-driven Design
<p>This repo contains all supplementary data sets that we have created and used throughout this thesis. In particular, it contains<br> - expert interview material (elicitation): consent form and question catalogue for requirements elicitation<br> - expert interview material (evaluation): consent form and task description for expert evaluation<br> - Diagrams related to our modeling concept and Dqualizer<br> - Screenshots of the Domain Story Modeler with our Modeling Concept</p>
Non-Functional Requirements for Machine Learning: Understanding Current Use and Challenges Among Practitioners
<p>This package contains interview data (themes, codes, and quotes) and survey data to identify, define and measure non-functional requirements for ML systems.</p>
Continuous Non-Functional Requirements: Practices, Opportunities, and Trade-offs for Small, Agile Organizations
<p>This data represents two artifacts from our research in evaluating how small, agile organizations manage non-functional requirements in conjunction with continuous practices. It includes our base interview questions and the resulting codebook.</p> <p>This page includes supplementary materials associated with our paper entitled "Continuous Non-Functional Requirements: Practices, Opportunities, and Trade-offs for Small, Agile Organizations".</p>
Automatic Classification of Non-functional Requirements in App User Reviews Based on System Model and Artificial Intelligence
<p>This is the replication package for the paper: "Automatic Classification of Non-functional Requirements in App User Reviews Based on System Model and Artificial Intelligence". It contains the dataset of our experiment for the replication by other researchers. In the meanwhile, we provide brief description of the files in the replication package in the following.</p> <p><strong>1. dataset folder</strong></p> <ul> <li>dataset_user_reviews.xlsx contains 1278 labelled non-requirement user reviews.</li> <li>readme.txt describes the meaning of the data in dataset_user_reviews.xlsx in detail.</li> </ul>
ReSpaN - Spanish Dataset for non-functional requirements classification
<p>The ReSpaN (Spanish Dataset for non-functional requirements classification) dataset is conformed by requirements collected from final degree projects from one University. Manual labeling was performed by 7 annotators in such a way that each requirement had at least three labels. The labels were the categories and subcategories of the ISO/IEC 25010 quality model. The definitions of each category and subcategory are available at https://iso25000.com/index.php/normas-iso-25000/iso-25010, in both Spanish and English. The label ’No agreement’ was used for requirements with no majority in the labeling process. The final classification of each requirement is based on unanimity or majority. This dataset was created following the FAIR principles. </p> <p>Cited as:</p> <p>Maria-Isabel Limaylla-Lunarejo, Nelly Condori-Fernandez, and Miguel R. Luaces. 2023. Towards a FAIR Dataset for non-functional requirements. In Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing (SAC '23). Association for Computing Machinery, New York, NY, USA, 1414–1421. https://doi.org/10.1145/3555776.3578611</p>
Continuous Non-Functional Requirements: Practices, Opportunities, and Trade-Offs for Small, Agile Organizations
<p>Contains the interview questions and codebook (including code counts) produced by our research.</p>
Data for: Evaluating Different i*-based Approaches for Selecting Functional Requirements while Balancing and Optimizing Non-Functional Requirements: A Controlled Experiment
<p>This dataset is associated with the study published in <em>Information and Software Technology</em>, Volume 106, February 2019, Pages 68-84. The study investigates the effectiveness of an alternative automated i* post-processed model for requirements engineering. The primary objective of the study was to provide empirical evidence that requirement engineers may perform better at selecting Functional Requirements (FRs) while optimizing and balancing Non-Functional Requirements (NFRs) using this post-processed model, compared to the original i* model.</p> <p>The data in this dataset was used for the analyses presented in the publication and includes the inputs required to evaluate the performance of requirement engineers in selecting FRs and optimizing NFRs. It provides the basis for comparing the decision-making efficiency and accuracy in FR selection, as well as the optimization process for NFRs in both the original and automated post-processed i* models.</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>
Towards Explainable Classification of Non-Functional Requirements Using Fine-Tune-Chain-of-Thought Reasoning - Repo 2
<p>Supplementary material for <em>Towards Explainable Classification of Non-Functional Requirements Using Fine-Tune-Chain-of-Thought Reasoning </em></p> <div> <div> </div> <div>This version contains the llama2-7B model generated output for NFR identification on the PURE dataset. As discussed, the llama2-7B model mostly generated extraneous text. </div> <div> </div> <div>Llama2-7B generated output can be found in the column > <em>Model_output</em></div> <div>Input to Llama2-7B column > <em>llama_input_pure_test</em></div> <div> </div> <div><strong>Here is the link to repo 1 for accessing the fine-tuned models, code, and datasets > <a href="../records/10574330">https://zenodo.org/records/10574330</a></strong></div> </div>
Spanish Dataset for non-functional requirements classification
<p>This dataset is conformed by requirements collected from final degree projects from one University. Manual labeling was performed by 7 annotators in such a way that each requirement had at least 3 labels. The labels were the categories and subcategories of the ISO/IEC 25010 quality model. The label ’No agreement’ was used for requirements with no majority in the labeling process. The final classification of each requirement is based on unanimity or majority. This dataset was created following the FAIR principles.</p>
Spanish Dataset for non-functional requirements classification
<p>NFR Spanish dataset is conformed by requirements collected from final degree projects from one University. Manual labeling was performed by 7 annotators in such a way that each requirement had at least 3 labels. The labels were the categories and subcategories of the ISO/IEC 25010 quality model. The label ’No agreement’ was used for requirements with no majority in the labeling process. The final classification of each requirement is based on unanimity or majority. This dataset was created following the FAIR principles. </p>
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