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16 results for “Architectural Smells”
Investigating instability architectural smells evolution: an exploratory case study
<p>This is the dataset used in our case study on architectural smells evolution. We tracked smells from 524 versions among 14 open source Java systems. </p> <p>More information can be found in our ICSME'19 paper titled: "Investigating instability architectural smells evolution: an exploratory case study".</p> <p>Additionally, you can find the tool on GitHub: <a href="https://github.com/darius-sas/astracker">https://github.com/darius-sas/astracker</a></p>
Dataset: an empirical study on architectural smells through a pipeline for continuous technical debt assessment
<h2><strong>Dataset of the study "An empirical study on architectural smells through a pipeline for continuous technical debt assessment"</strong></h2> <h3><strong>Abstract</strong></h3> <p>In recent years, researchers spent an increasing amount of effort investigating technical debt, with quantitative methods, and in particular static analysis, being the most common approach to investigate such a topic.</p> <p>However, quantitative studies are susceptible, to varying degrees, to external validity threats, which hinder the generalisation of their findings.<br>In response to this concern, researchers strive to expand the scope of their studies by incorporating a larger number of projects into their analyses. This practice is typically executed on a case-by-case basis, necessitating substantial data collection efforts that have to be repeated for each new study.</p> <p>To address this issue, this paper presents an approach for tackling this problem and enabling researchers to study architectural smells, a well-known indicator of architectural technical debt, at a large scale. Specifically, we introduce a novel approach to a data collection pipeline that leverages Apache Airflow to continuously generate up-to-date, large-scale datasets with any static analysis tool.</p> <p>Finally, we use the data collected through the pipeline to study the correlation between architectural smells and logical coupling in order to understand how smells influence maintenance efforts.</p>
Additional Material for Architecture Smell Ontology
<p>This is the dataset of our literature review study on Architecture Smells. We provide the decisive exclusion criteria for excluded studies and the classification for included studies. We exported OWL file of the ontology from Protégé and included the tables of the architecture smells and the connected quality attributes and design principles. The study protocol is also included.</p> <p> </p>
Tailoring Code Smells to Model-View-Controller Architectures: Online appendix
<p>Appendix for our paper "Tailoring Code Smells to Model-View-Controller Architectures" (EMSE journal)</p>
Featured Scents: Assessing Architectural Smells for Self-Adaptive Systems at Runtime
<p>Self-adaptive systems (SAS) change their behavior and structure at runtime to answer the changes in their environment. Such systems combine different architectural fragments or solutions via feature binding/unbinding at runtime. Moreover, this combination may negatively impact the system's architectural qualities, exhibiting architectural bad smells (ABS). These issues are challenging to detect in the code due to the combinatorial explosion of interactions amongst features. Since SAS do not document these features in their source code, design time smell detection ignores them and risks reporting spurious smells. This paper assesses this risk to understand how ABS occur at runtime for different feature combinations. We look for cyclic dependency and hub-like ABS in various runtime adaptations of two SAS, Adasim and mRubis. Our results indicate that architectural smells are feature-dependent and that their number is highly variable from one adaptation to the other. Some ABS appear in all runtime adaptations, some in only a few. We discuss the reasons behind these architectural smells for each system and draw some lessons for targeted analyses of ABS in SAS.</p>
Replication Package for "On the correlation between Architectural Smells and Static Analysis Warnings"
<div> <div> <div> <div> <div> <h1>Replication Package for the Paper: "On the Relation between Architectural Smells and Static Analysis Warnings"</h1> <h2>Authors</h2> <ul> <li>Matteo Esposito, University of Oulu, Finland</li> <li>Mikel Robredo, University of Oulu, Finland</li> <li>Francesca Arcelli Fontana, University of Milano-Bicocca, Italy</li> <li>Valentina Lenarduzzi, University of Oulu, Finland</li> </ul> <h2>Content Overview</h2> <p>This replication package contains the following materials:</p> <ul> <li><strong>Tables:</strong> Excel files that include all hypothesis testing data, including normality tests for each hypothesis.</li> <li><strong>Data:</strong> RAW Qualitas Corpus dataset and aggregated SAT output.</li> <li><strong>SAT Instructions:</strong> A PDF file providing detailed instructions for the SAT setup and execution.</li> </ul> <h2>Contact Information</h2> <p>For any issues, questions, or further assistance, please do not hesitate to contact the authors of the paper. We are here to help!</p> </div> </div> </div> </div> </div>
[Replication Package] Impact of Architectural Smells on Software Performance
<p>Replication Package of the paper "Impact of Architectural Smells on Software Performance: an Exploratory Study" accepted for presentation to the 27th International Conference on Evaluation and Assessment in Software Engineering (EASE) 2023.</p>
Investigating Non-Usually Employed Features in the Identification of Architectural Smells: A Machine Learning-Based Approach
<p>Architectural smells (ASs) negatively affect the maintenance and evolution of software at the architectural level. Most of the current approaches for ASs identification rely on the same small and well-known set of usually employed metrics (UE-Ms) with fixed thresholds. Machine learning (ML) is a promising technique for smell identification as algorithms can learn from a rich set of metrics/features, covering several characteristics of the software and incorporating a certain degree of subjectivity. This has been explored by building datasets with a robust and rich set of features, including not only the UE-Ms but also other non-usually employed metrics (NUE-Ms). However, usually the UE-Ms determine the output of the algorithms, obfuscating other metrics that have the potential to improve the classification. This also leads to inflated and difficult to maintain datasets. <br> In this paper, we investigate the accuracy of some ML algorithms employing only NUE-Ms. We scoped our study in the classification of two smells: God Component and Unstable Dependency. This investigation revealed a set of NUE-Ms that can be also used to identify these smells and the contribution of each one for the classification. Based on this information, software engineers can then build a final dataset just with the potential features. We also briefly present our tool, called InSet, that was used by academics and practitioners to identify smells in their systems. The feedback of them was used as the oracle to compare our tool to other approaches and good results were reached. </p>
Replication Package: Architectural Smell Detection
<p>Replication package of architectural smell detection paper</p>
Benchmark: Architectural Smell ICSE 2024
<p>Benchmark of Architectural Smell ICSE 2024 paper</p>
Benchmark: Architectural Smell ICSE 2024
<p>Benchmark of Architectural Smell ICSE 2024 paper</p>
Replication Package: Architectural Smell Detection
<p>Replication package of architectural smell detection paper</p>
Supplementary Material for A Systematic Mapping Study on Architectural Smells Detection
<p>This is the supplementary material for a systematic mapping study on architectural smells detection.The material is in the form of a spreadsheet. First sheet is for detection techniques and second for detection tools.</p>
InSet: A Tool to Identify Architecture Smells Using Machine Learning
<p>Architectural smells (ASs) are architectural decisions that negatively affect the maintenance and evolution of software. Most of the existing tools able to identify AS rely on few metrics with fixed thresholds. However, it is not possible to define specific metrics and thresholds that meet all the cases, i.e., the classification of a piece of code in smell or not can depend on the domain, the experience of developers, organization patterns or even from a vast set of features - so there is a subjective ingredient in this decision.<br> Machine Learning (ML) can help to make these decisions/classifications more precise by taking into consideration a vast set of features and also feedback from experts.<br> This paper presents a machine learning-based tool to detect the architectural smells Unstable Dependency(UD) and God Component(GC). Our tool is able to take into consideration users' feedback to retrain the algorithms and constantly improve their performance. Our tool got good result in terms of accuracy, precision, recall, F-measure and Kappa's coefficient.</p>
Dataset: Architectural Smell Detection
<p>Dataset of architectural smell detection paper</p>
A Validated Set of Smells in Model-View-Controller Architectures: ICSME 2016 appendix
<p>Appendix of our ICSME 2016 paper.</p>
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