UML diagrams present the graphical representation of the system. Model-driven testing not only helps in early identification of faults but also results in reducing the testing effort at the later stages of SDLC. This paper intends to identify and make a critical review of different techniques for test case generation using UML activity diagrams (UAD). System activity diagram is used to depict the different dynamic aspects of the system. UAD not only presents the sequential or concurrent activities but also presents the conditional and parallel activities. For this literature survey different aspects like test case generation, test automation, and test case prioritization & minimization using UAD has been explored. The analysis of the literature portrays that extensive literature exists regarding automation of the testing using various aspects of activity diagrams. Similarly, test cases prioritization has also been explored using the activity diagrams incorporating manual, automated and semiautomated techniques.
Abstract:Scientific papers hold an association with previous research contributions (i.e. books, journals or conference papers, and web resources) in the form of citations. Citations are deemed as a link or relatedness of the previous work to the cited work. The nature of the cited material could be supportive (positive), contrastive (negative), or objective (neutral). Extraction of the author's sentiment towards the cited scientific articles is an emerging research discipline due to various linguistic differences between the citation sentences and other domains of sentiment analysis. In this paper, we propose a technique for the identification of the sentiment of the citing author towards the cited paper by extracting unigram, bigram, trigram, and pentagram adjective and adverb patterns from the citation text. After POS tagging of the citation text, we use the sentence parser for the extraction of linguistic features comprising adjectives, adverbs, and n-grams from the citation text. A sentiment score is then assigned to distinguish them as positive, negative, and neutral.In addition, the proposed technique is compared with manually classified citation text and 2 commercial tools, namely SEMANTRIA and THEYSAY, to determine their applicability to the citation corpus. These tools are based on different techniques for determining the sentiment orientation of the sentence. Analysis of the results shows that our proposed approach has achieved results comparable to the commercial counterparts with average precision, recall, and accuracy of 90%, 81.82%, and 85.91% respectively.
Adopting open source software from the Internet, developers often encounter the problem of accessing the quality of candidate software. To efficiently adopt the system they need a sort of quality guarantee regarding software resources. To assist the developer in software adoption evaluation we have proposed a software adoption assessment approach based on user comments. In our proposed approach, we first collected the textual reviews regarding the software resource, assigned the sentiment polarity (positive or negative) to each comment, extracted the adoption aspect which the comment talks about, and then based on the adoption aspects of the software generated an aggregated sentiment profile of the software. Twitter micro-blogging data about OSS products were crawled, preprocessed, tagged, and then summarized. To evaluate the proposed model, a set of experiments was designed and conducted using different classifiers, i.e. Apriori, GSP, and AdaBoost. For the feature level sentiment summarization we have used Bayesian statistics and frequency distribution techniques. The results show that the proposed approach achieved satisfying precision and recall, i.e. above 80% along with an average accuracy of 70.98%.
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