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4 results for “traceability recovery”
Dataset for Requirements Classification in Traceability Link Recovery Datasets
<p>The dataset contains a gold standard for classifying parts of requirements in five traceability link recovery benchmark datasets.</p> <p><strong>Classification</strong></p> <ul> <li>For aspect classification: <ul> <li>functional aspects (<strong>F</strong>)</li> <li>quality aspects (<strong>Q</strong>)</li> </ul> </li> <li>For concerns in functional requirements (c.f. <a href="https://doi.org/10.1109/RE48521.2020.00028">NoRBERT publication</a>): <ul> <li><strong>Function</strong>: A function that a system shall perform</li> <li><strong>Behavior</strong>: Behavior, the system displays or reactions that are triggered by one or more stimuli</li> <li><strong>Data</strong>: A data item or data structure that shall be part of a system's state</li> <li><strong>UserRelated</strong>: Behavior of the user or functionality of the system attributable to the user</li> </ul> </li> </ul> <p><strong>Datasets</strong></p> <p>The dataset comprises preprocessed requirements of the eTour, iTrust, SMOS, eAnci and LibEST datasets. As SMOS and eAnci's original requirements were written in Italian, the dataset comprises automatically translated versions of the requirements to English. The datasets were retrieved from the <a href="http://coest.org/">website</a> of the Center of Excellence for Software & Systems Traceability (CoEST). Attribution for the datasets:</p> <p>The original eTour dataset was provided for the TEFSE challenge at 6th International Workshop on Traceability in Emerging Forms of Software Engineering (TEFSE), 2011 and was retrieved from <a href="http://coest.org/">http://coest.org/</a></p> <p>The iTrust dataset was retrieved from <a href="http://coest.org/">http://coest.org/</a></p> <p>The original SMOS and eAnci datasets can be attributed to Gethers et al., On integrating orthogonal information retrieval methods to improve traceability recovery. In 2011 27th IEEE International Conference on Software Maintenance (ICSM), Sep. 2011 and were retrieved from <a href="http://coest.org/">http://coest.org/</a> </p> <p>The LibEST dataset can be attributed to Moran et al., Improving the Effectiveness of Traceability Link Recovery using Hierarchical Bayesian Networks. In 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE), May 2020 and was retrieved from <a href="https://gitlab.com/SEMERU-Code-Public/Data/icse20-comet-data-replication-package">https://gitlab.com/SEMERU-Code-Public/Data/icse20-comet-data-replication-package</a></p> <p> </p>
An Approach for Traceability Recovery between Bug Reports and Test Cases
<p>Scripts and data sets used in research for <em>An Approach for Traceability Recovery between Bug Reports and Test Cases</em>.</p> <p><em><strong>(Context)</strong></em> Automatic traceability recovery between software artifacts may promote early detection of issues.<br> Information Retrieval (IR) techniques have been proposed for the task, but they differ considerably in terms of input parameters and results. It is difficult to assess results when those techniques are applied in isolation, usually in small or medium-sized software projects. Also, an overview would be more comprehensive if a Deep Learning (DL) based technique is applied, in comparison with traditional IR techniques.<br> <em><strong>(Objective)</strong></em> We propose an approach to recover traceability links between bug reports and test cases, which can be instantiated with a set of IR and DL techniques.<br> <em><strong>(Method)</strong></em> For applying and evaluating our solution, we used historical data from the Mozilla Firefox quality assurance (QA) team, on which we assessed the following IR techniques: LSI, LDA, and BM25. We also experimented with a DL architecture called Convolutional Neural Networks (CNNs) through the use of Word Embeddings.<br> <em><strong>(Results)</strong></em> In this context of traceability, we noticed poor performances from three out of the four studied techniques. Only the LSI technique was effective, even standing out over the state-of-the-art BM25 technique.<br> <em><strong>(Conclusions)</strong></em> The obtained results suggest that the semi-automatic application of the LSI technique -- with an appropriate combination of thresholds -- is feasible for real-world software projects.</p>
Assessing Word Similarity Metrics for Traceability Link Recovery - Evaluation Dataset
<p>This dataset includes all data that was used for the evaluation of my bachelor's thesis:</p> <p><em>Assessing Word Similarity Metrics for Traceability Link Recovery</em></p> <p>The following files correspond to the following data sets from the evaluation:</p> <ul> <li>cc-en-300.tar.gz corresponds to fastText's cc.en.300.bin embedding</li> <li>crawl-300d-2M-subword.tar.gz corresponds to fastText's crawl-300d-2M-subword.bin embedding</li> <li>wiki-news-300d-1M-subword.tar.gz corresponds to fastText's wiki-news-300d-1M-subword.bin embedding</li> <li>wordnet.tar.gz corresponds to the WordNet 3.1 semantic network</li> <li>sewordsim.tar.gz corresponds to SEWordSimDB's vector similarity database</li> <li>glove_cc_840B_300d.tar.gz corresponds to GloVe's CC vector embedding</li> <li>glove_wikigiga_300d.tar.gz corresponds to GloVe's 300 dimensional WIGI vector embedding</li> <li>glove_wikigiga_200d.tar.gz corresponds to GloVe's 200 dimensional WIGI vector embedding</li> <li>glove_wikigiga_100d.tar.gz corresponds to GloVe's 100 dimensional WIGI vector embedding</li> <li>glove_wikigiga_50d.tar.gz corresponds to GloVe's 50 dimensional WIGI vector embedding</li> <li>glove_twitter_200d.tar.gz corresponds to GloVe's 200 dimensional TWTR vector embedding</li> <li>glove_twitter_100d.tar.gz corresponds to GloVe's 100 dimensional TWTR vector embedding</li> <li>glove_twitter_50d.tar.gz corresponds to GloVe's 50 dimensional TWTR vector embedding</li> <li>glove_twitter_25d.tar.gz corresponds to GloVe's 25 dimensional TWTR vector embedding</li> <li>eval_results.tar.gz contains the detailed evaluation results for each configuration of all measures</li> </ul> <p>The licenses of all data sets are included in their respective files.</p> <p>Some of these data sets are .sql files. To use these files to reproduce the evaluation, they need to be imported into a sqlite3 database. The version of ArDoCo used for the evaluation is only able to work with sqlite3 databases and not with sql files.</p>
Improving Traceability Recovery Between Bug Reports and Manual Test Cases
<p>Vídeo backup da apresentação SBES 2020.</p> <p>Artigo: Improving Traceability Recovery Between Bug Report and Manual Test Cases.</p>
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