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4 results for “software aging”
Data set for the paper "What are the Effects of History Length and Age on Mining Software Change Impact?"
<p>Data set for the paper What are the Effects of History Length and Age on Mining Software Change Impact?<br> by Leon Moonen, Thomas Rolfsnes, David Binkley and Stefano di Alesio.<br> In Journal of Empirical Software Engineering (EMSE), 2018, Springer. https://doi.org/10.1007/s10664-017-9588-z<br> Available from https://evolveit.bitbucket.io/publications/emse2018/</p> <p>Please cite this work by referring to the corresponding journal publication (a preprint is included in this package).</p> <p>The goal of Software Change Impact Analysis is to identify artifacts (typically source-code files or individual methods therein) potentially affected by a change. Recently, there has been increased interest in <em>mining</em> software change impact based on evolutionary coupling. A particularly promising approach uses association rule mining to uncover potentially affected artifacts from patterns in the system’s change history. Two main considerations when using this approach are the <em>history length</em>, the number of transactions from the change history used to identify the impact of a change, and <em>history age</em>, the number of transactions that have occurred since patterns were last mined from the history. Although history length and age can significantly affect the quality of mining results, few guidelines exist on how to best select appropriate values for these two parameters.</p> <p>In this paper, we empirically investigate the effects of history length and age on the quality of change impact analysis using mined evolutionary coupling. Specifically, we report on a series of systematic experiments using three state-of-the-art mining algorithms that involve the change histories of two large industrial systems and 17 large open source systems. In these experiments, we vary the length and age of the history used to mine software change impact, and assess how this affects precision and applicability. Results from the study are used to derive practical guidelines for choosing history length and age when applying association rule mining to conduct software change impact analysis. </p>
Aging-related bugs and software complexity metrics
<p>This dataset contains information on aging-related bugs found in two open-source projects (the Linux kernel and the MySQL DBMS). This dataset has been used to investigate defect prediction approaches for aging-related bugs, by using software complexity metrics and machine learning techniques. New software complexity metrics were proposed in this study to support defect prediction ("aging-related" metrics).</p> <p>The dataset contains an ARFF file for each subsystem of the open-source projects. Each row of the ARFF file contains:</p> <p>- The name of a file in the project;</p> <p>- "Program size" metrics for the file (columns from 2 to 50);</p> <p>- "McCabe's cyclomatic complexity" metrics for the file (columns from 51 to 68);</p> <p>- "Halstead" metrics for the file (columns from 69 to 77);</p> <p>- "Aging-related" metrics for the file (columns from 78 to 83);</p> <p>- The number of aging-related bugs found in the file.<br> </p>
Use of Cognitive Stimulation Software for Patients Over the Age of 70 Followed for Breast Cancer: COG-TAB-AGE Feasibility Study
ClinicalTrials.gov study NCT04261153. IPD Sharing: NO. Countries: 1. Publications: 1.
Performance of the Diagnostic Value of Bone Age Assessment Software Based on Deep Learning in Chinese Children
ClinicalTrials.gov study NCT05137301. IPD Sharing: NO. Countries: 1. Publications: 0.
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