@article {814,
	title = {The additional testsuite framework: facilitating software testing and test management},
	journal = {International Journal of Web Engineering and Technology},
	volume = {TBD},
	year = {2022},
	abstract = {<p>In this paper, we present the Additional Testsuite Framework, a novel test suite management approach, which provides structures and instrumentation for the creation, maintenance, evolution and use of test suites for software programs. In particular, the tests can be maintained in a centralized repository, and are developed and maintained independently of specific versions of the associated software. Through the use of annotations, tests are categorized and distributed to the desired versions of the software. The presented framework also supports test-based development, dynamic/selective program builds, feature-based builds, testing in different environments and source code analysis. The Additional Testsuite Framework concept has been implemented and extensively evaluated, with the test cases notably including the JBoss EAP CE and OpenLiberty servers.</p>
},
	keywords = {Additional Testsuite Framework, Dynamic Testing, Feature-based testing, Internet application testing, Multiple Environment Testing, Multiversion Testing, Test program repository, Test-Driven Development, Testsuite Management},
	author = {Sotiropoulos, Panagiotis and Vassilakis, Costas}
}
@article {797,
	title = {On Addressing the Low Rating Prediction Coverage in Sparse Datasets Using Virtual Ratings},
	journal = {SN Computer Science},
	volume = {2},
	year = {2021},
	month = {May},
	pages = {255},
	abstract = {<p>Collaborative filtering-based recommendation systems consider users{\textquoteright} likings and interests, articulated as ratings within a database to offer personalized recommendations. Unfortunately, many collaborative filtering datasets exhibit the {\textquoteleft}{\textquoteleft}grey sheep{\textquoteright}{\textquoteright} phenomenon, a state where no near neighbours can be found for certain users. This phenomenon is extremely frequent in datasets where users, on average, have rated only a small percentage of the available items, which are termed as sparse datasets. This paper addresses the {\textquoteleft}{\textquoteleft}grey sheep{\textquoteright}{\textquoteright} problem by proposing the virtual ratings concept and introduces an algorithm for virtual rating creation on the basis of actual ratings. The novelty behind this concept is that the introduction of the virtual ratings effectively reduces the user{\textendash}item rating matrix sparsity, thus alleviating the aforementioned problem. The proposed algorithm, which is termed as CFVR, has been extensively evaluated and the results show that it achieves to considerably improve the capability of a collaborative filtering system to formulate tailored recommendations for each user, when operating on sparse datasets, while at the same time improves rating prediction quality.</p>
},
	keywords = {collaborative filtering, Cosine Similarity, Evaluation, Pearson Correlation Coefficient, Recommender Systems, Sparse Datasets, Virtual ratings},
	issn = {2661-8907},
	doi = {10.1007/s42979-021-00668-8},
	url = {https://doi.org/10.1007/s42979-021-00668-8},
	author = {Margaris, Dionisis and Spiliotopoulos, Dimitris and Karagiorgos, Gregory and Vassilakis, Costas and Vasilopoulos, Dionysios}
}
