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7 results for “Architectural Design Decisions”
Architectural Design Decisions for the Machine Learning Workflow: Dataset and Code
<p><strong>Title:</strong> Architectural Design Decisions for the Machine Learning Workflow: Dataset and Code</p> <p><strong>Authors:</strong> Stephen John Warnett; Uwe Zdun</p> <p><strong>About:</strong> This is the dataset and code artifact for the article entitled "Architectural Design Decisions for the Machine Learning Workflow".</p> <p><strong>Contents:</strong> The "_generated" directory contains the generated results, including latex files with tables for use in publications and the Architectural Design Decision model in textual and graphical form. "Generators" contains Python applications that can be run to generate the above. "Metamodels" contains a Python file with type definitions. "Sources_coding" contains our source codings and audit trail. "Add_models" contains the Python implementation of our model and source codings. Finally, "appendix" contains a detailed description of our research method.</p> <p><strong>Article Abstract: </strong>Bringing machine learning models to production is challenging as it is often fraught with uncertainty and confusion, partially due to the disparity between software engineering and machine learning practices, but also due to knowledge gaps on the level of the individual practitioner. We conducted a qualitative investigation into the architectural decisions faced by practitioners as documented in gray literature based on Straussian Grounded Theory and modeled current practices in machine learning. Our novel Architectural Design Decision model is based on current practitioner understanding of the topic and helps bridge the gap between science and practice, foster scientific understanding of the subject, and support practitioners via the integration and consolidation of the myriad decisions they face. We describe a subset of the Architectural Design Decisions that were modeled, discuss uses for the model, and outline areas in which further research may be pursued.</p> <p><strong>Objective:</strong> This article aims to study current practitioner understanding of architectural concepts associated with data processing, model building, and Automated Machine Learning (AutoML) within the context of the machine learning workflow.</p> <p><strong>Method:</strong> Applying Straussian Grounded Theory to gray literature sources containing practitioner views on machine learning practices, we studied methods and techniques currently applied by practitioners in the context of machine learning solution development and gained valuable insights into the software engineering and architectural state of the art as applied to ML.</p> <p><strong>Results:</strong> Our study resulted in a model of Architectural Design Decisions, practitioner practices, and decision drivers in the field of software engineering and software architecture for machine learning.</p> <p><strong>Conclusions:</strong> The resulting Architectural Design Decisions model can help researchers better understand practitioners' needs and the challenges they face, and guide their decisions based on existing practices. The study also opens new avenues for further research in the field, and the design guidance provided by our model can also help reduce design effort and risk. In future work, we plan on using our findings to provide automated design advice to machine learning engineers.</p>
Architectural Design Decisions for Machine Learning Deployment: Dataset and Code
<p><strong>Title:</strong> Architectural Design Decisions for Machine Learning Deployment: Dataset and Code</p> <p><strong>Authors:</strong> Stephen John Warnett; Uwe Zdun</p> <p><strong>About:</strong> This is the dataset and code artefact for the paper entitled "Architectural Design Decisions for Machine Learning Deployment".</p> <p><strong>Contents:</strong> The "_generated" directory contains the generated results, including latex files with tables for use in publications and the Architectural Design Decision model in textual and graphical form. "Generators" contains Python applications that can be run to generate the above. "Metamodels" contains a Python file with type definitions. "Sources_coding" contains our source codings and audit trail. "Add_models" contains the Python implementation of our model and source codings. Finally, "appendix" contains a detailed description of our research method.</p> <p><strong>Paper Abstract:</strong> Deploying machine learning models to production is challenging, partially due to the misalignment between software engineering and machine learning disciplines but also due to potential practitioner knowledge gaps. To reduce this gap and guide decision-making, we conducted a qualitative investigation into the technical challenges faced by practitioners based on studying the grey literature and applying the Straussian Grounded Theory research method. We modelled current practices in machine learning, resulting in a UML-based architectural design decision model based on current practitioner understanding of the domain and a subset of the decision space and identified seven architectural design decisions, various relations between them, twenty-six decision options and forty-four decision drivers in thirty-five sources. Our results intend to help bridge the gap between science and practice, increase understanding of how practitioners approach the deployment of their solutions, and support practitioners in their decision-making.</p> <p><strong>Objective:</strong> This paper aims to study current practitioner understanding of architectural concepts associated with machine learning deployment.</p> <p><strong>Method:</strong> Applying Straussian Grounded Theory to gray literature sources containing practitioner views on machine learning practices, we studied methods and techniques currently applied by practitioners in the context of machine learning solution development and gained valuable insights into the software engineering and architectural state of the art as applied to ML.</p> <p><strong>Results:</strong> Our study resulted in a model of Architectural Design Decisions, practitioner practices, and decision drivers in the field of software engineering and software architecture for machine learning.</p> <p><strong>Conclusions:</strong> The resulting Architectural Design Decisions model can help researchers better understand practitioners' needs and the challenges they face, and guide their decisions based on existing practices. The study also opens new avenues for further research in the field, and the design guidance provided by our model can also help reduce design effort and risk. In future work, we plan on using our findings to provide automated design advice to machine learning engineers.</p>
API Description-Based Conformance Assessment of Architectural Design Decision: Dataset and Code
<p>This is the dataset and related code artifact for the article "API Description-Based Conformance Assessment of Architectural Design Decision".</p> <p><strong>Abstract of the article:</strong></p> <p>Microservice APIs are often designed based on Domain-Driven Design. It can be challenging to judge the quality<br> of the relation between such a design and the implemented API, especially when facing frequent releases and changes in an API and its design models. Conformance assessment aims to improve software quality by bridging the gap between design and implementation. However, manual conformance assessment is time-intensive and error prone. This paper proposes a novel approach for automated conformance assessment of API designs in relation to API design decisions. Our approach aims to provide<br> the first fully automated conformance assessment approach based solely on the code of API Descriptions such as OpenAPI. We<br> applied and exemplified our approach for checking conformance to the patterns and practices in an API design decision for mapping links in a system’s domain model to API representations. The total accuracy score (F1-measure) for decision option identification in our multi-case study for this decision is 97.22%.</p>
Conformance Assessment of Architectural Design Decisions on API Endpoint Designs Derived from Domain Models: Dataset and Code
<p>This is the dataset and related code artifact for the article "Conformance Assessment of Architectural Design Decisions on API Endpoint Designs Derived from Domain Models".</p> <p><strong>Abstract of the article:</strong></p> <p>Domain-driven design (DDD) is commonly used, especially in enterprise systems, to design microservices. A crucial aspects of microservice design is API design, which includes the design of API endpoints. In particular, we studied link mapping, API operation design, and resource segregation as API endpoint design issues that are linked to domain model design. Based on Architectural Design Decisions (ADD) studied in a prior empirical study on the interrelation of DDD and APIs, we suggest a new approach for the automated assessment of conformance to ADD options. This approach aims to support the continuous analysis of API endpoint designs. The approach suggests automated detectors to detect ADD options selected in a given API endpoint design, as well as an assessment scoring scheme based on our empirical results. For evaluation of our results, we first manually created a ground truth for 12 cases in a multi-case study, and then compared the results of our automated detectors to the ground truth for each of those cases.</p>
Conformance Assessment of Architectural Design Decisions on the Mapping of Domain Model Elements to APIs and API Endpoints: Dataset and Code
<p>This is the dataset and related code artifact for the article "Conformance Assessment of Architectural Design Decisions on the Mapping of Domain Model Elements to APIs and API Endpoints".</p> <p><strong>Abstract of the article:</strong></p> <p> Microservice APIs are often identified and designed based on Domain-Driven Design (DDD). To help in the continuous analysis of mappings of domain model elements to APIs and API endpoints, we aim to automate the assessment of conformance to Architectural Design Decision (ADD) options. The ADDs, their decision options, and relevant decision drivers studied in this paper originate from an empirical study on the mapping of domain model elements to APIs and API endpoints in practice. We propose automated detectors to detect the decision options of the ADDs taken in a given microservice API model, and an assessment scoring scheme based on the empirical knowledge codified in the ADDs. We evaluate our work, by first manually creating a ground truth for 14 cases in a multi-case study, and then comparing the results of our automated detectors to the ground truth for each of the 14 cases. In the cases we were able to identify 86\% of the decision points correctly, and a statistical analysis of our data shows only a negligible effect size for differences to the ground truth.</p>
Developing Deep Learning Approaches to Find and Classify Architectural Design Decisions in Issue Tracking Systems
<p>This upload contains three files:</p> <ol> <li>mongodump-JiraRepos_2023-03-07-16 00.archive: Archive containing the issue data pulled from the Jira API.</li> <li>mongodump-MiningDesignDecisions.archive: Archive containing the data of our deep learning models and the labelled issues.</li> <li>mongodump-MiningDesignDecisions-lite.archive: Similar to the archive above, except this one only contains the best trained model (BERT). Also, it does not contain any embeddings or other files.</li> </ol> <p>This archive contains the data of our deep learning models and the labelled issues (MiningDesignDecisions archive).</p>
Exploring Architectural Design Decisions in Mailing Lists and their Traceability to Issue Trackers
<p>The online repository for the paper: Exploring Architectural Design Decisions in Mailing Lists and their Traceability to Issue Trackers, published in ECSA 2024.</p> <p>The online repo has the following folders:</p> <p>1) Classifier: it contains the source code of the classifier and quantitative analysis. Furthermore, there are sufficient details on the classifier performance, and how to replicate the results.</p> <p>2) Dataset: it contains the datasets we used to perform our analysis. This involves dataset before and after BERT classification, as well as exported json files used for training and analysis. The dataset in a zip file. This is a MySQL database in a zip file. It can be opened separately or opened using the search tool.</p> <p>3) Qualitative analysis: it contains coding book of design decisions in mailing lists as well as coding book of methods to discuss ADDs between emails and issues, as well as precision charts for the applied similarity algorithms.</p> <p>4) Searching tool: it contains the jar file and source code of the searching tool. The tool used to annotate and search for emails. It can be used to open the dataset from the zip directly. The tool is a jar file which can be run directly through a double click. The tool is tested on Windows and Linux. The folder also contains keywords to search for architectural emails, as well as documentation.</p>
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