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See detailÉcriture du genre, genre de l'écriture
Barthelmebs-Raguin, Hélène UL

Book published by Peter Lang (in press)

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See detailEmpirical Evaluation of Mutation-based Test Prioritization Techniques
Shin, Donghwan; Yoo, Shin; Papadakis, Mike UL et al

in Software Testing, Verification and Reliability (in press)

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See detailWege zu einer Nachbarsprachen-/Grenz(raum)didaktik
Ehrhart, Sabine UL; Polzin-Haumann, Claudia; Putsche, Julia et al

in Romanistik und Angewandte Sprachwissenschaft (in press), 5

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See detailConclusion à Criminelles
Barthelmebs-Raguin, Hélène UL; Freyheit, Matthieu

in Barthelmebs-Raguin, Hélène; Freyheit, Matthieu (Eds.) Criminelles. Pourquoi les femmes tuent (in press)

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See detailCriminelles. Pourquoi les femmes tuent
Barthelmebs-Raguin, Hélène UL; Freyheit, Matthieu

Book published by Presses universitaires de Rennes (in press)

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See detailDire et faire percevoir la violence. Réflexions sur les écritures de Calixthe Beyala et Ananda Devi
Barthelmebs-Raguin, Hélène UL

in Barthelmebs-Raguin, Hélène; Freyheit, Matthieu (Eds.) Criminelles. Pourquoi les femmes tuent (in press)

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See detailEn mots et en images : le corps à l’œuvre chez Annie Ernaux
Barthelmebs-Raguin, Hélène UL

in Sens Public (in press)

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See detailIntroduction à Criminelles
Barthelmebs-Raguin, Hélène UL; Freyheit, Matthieu

in Barthelmebs-Raguin, Hélène; Freyheit (Eds.) Criminelles. Pourquoi les femmes tuent (in press)

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See detailÉcrire l’intime. Relation à l’écriture dans l’œuvre de Michèle Mailhot
Barthelmebs-Raguin, Hélène UL

in Verthuy, Maïr; Blais, Marie-Claire; Camet, Sylvie (Eds.) Collectif sur Michèle Mailhot (in press)

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See detailAn Active Learning Approach for Improving the Accuracy of Automated Domain Model Extraction
Arora, Chetan UL; Sabetzadeh, Mehrdad UL; Nejati, Shiva UL et al

in ACM Transactions on Software Engineering and Methodology (in press)

Domain models are a useful vehicle for making the interpretation and elaboration of natural-language requirements more precise. Advances in natural language processing (NLP) have made it possible to ... [more ▼]

Domain models are a useful vehicle for making the interpretation and elaboration of natural-language requirements more precise. Advances in natural language processing (NLP) have made it possible to automatically extract from requirements most of the information that is relevant to domain model construction. However, alongside the relevant information, NLP extracts from requirements a significant amount of information that is superfluous, i.e., not relevant to the domain model. Our objective in this article is to develop automated assistance for filtering the superfluous information extracted by NLP during domain model extraction. To this end, we devise an active-learning-based approach that iteratively learns from analysts’ feedback over the relevance and superfluousness of the extracted domain model elements, and uses this feedback to provide recommendations for filtering superfluous elements. We empirically evaluate our approach over three industrial case studies. Our results indicate that, once trained, our approach automatically detects an average of ≈ 45% of the superfluous elements with a precision of ≈ 96%. Since precision is very high, the automatic recommendations made by our approach are trustworthy. Consequently, analysts can dispose of a considerable fraction – nearly half – of the superfluous elements with minimal manual work. The results are particularly promising, as they should be considered in light of the non-negligible subjectivity that is inherently tied to the notion of relevance. [less ▲]

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See detailConstruction d’identités fédératrices en contexte multiculturel. Le cas du Livre de Dede Korkut
Barthelmebs-Raguin, Hélène UL

in Multiculturalisme, interculturalité, identité complexe : l’exemple du caucase (in press)

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See detail“D’après une histoire vraie”
Barthelmebs-Raguin, Hélène UL

in Ferry, Ariane; Provini, Sandra (Eds.) Figures et personnages de criminelles, des histoires tragiques au roman policier (in press)

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See detailFemmes fatales
Barthelmebs-Raguin, Hélène UL

in Dictionnaire raisonné de la peur (in press)

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See detailDatenbank für Zweisprachige Historische Luxemburgische Bekanntmachungen
Gilles, Peter UL; Ziegler, Evelyn

in Wallmeier, Nadine; Tophinke, Doris (Eds.) Historische Stadtsprachenforschung (in press)

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See detailLinguistic Landscape-Forschung in sprachhistorischer Perspektive: öffentliche Bekanntmachungen in der Stadt Luxemburg im langen 19. Jahrhundert
Gilles, Peter UL; Ziegler, Evelyn

in Zeitschrift für Germanistische Linguistik (in press)

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See detailLes contours des identités chez Anne-Lise Grobéty : pour un dépassement des frontières
Barthelmebs-Raguin, Hélène UL

in Bel, Jacqueline; Kuhnle, Till R (Eds.) Territoires et/ou frontières (in press)

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See detailLuxembourgish
Gilles, Peter UL

in Maitz, Péter; Boas, Hans C.; Deumert, Ana (Eds.) et al Varieties of German Worldwide (in press)

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See detailTest Suite Generation with the Many Independent Objective (MIO) Algorithm
Arcuri, Andrea UL

in Information and Software Technology (in press)

Context: Automatically generating test suites is intrinsically a multi-objective problem, as any of the testing targets (e.g, statements to execute or mutants to kill) is an objective on its own. Test ... [more ▼]

Context: Automatically generating test suites is intrinsically a multi-objective problem, as any of the testing targets (e.g, statements to execute or mutants to kill) is an objective on its own. Test suite generation has peculiarities that are quite different from other more regular optimisation problems. For example, given an existing test suite, one can add more tests to cover the remaining objectives. One would like the smallest number of small tests to cover as many objectives as possible, but that is a secondary goal compared to covering those targets in the first place. Furthermore, the amount of objectives in software testing can quickly become unmanageable, in the order of (tens/hundreds of) thousands, especially for system testing of industrial size systems. Objective: To overcome these issues, different techniques have been proposed, like for example the Whole Test Suite (WTS) approach and the Many-Objective Sorting Algorithm (MOSA). However, those techniques might not scale well to very large numbers of objectives and limited search budgets (a typical case in system testing). In this paper, we propose a novel algorithm, called Many Independent Objective (MIO) algorithm. This algorithm is designed and tailored based on the specific properties of test suite generation. Method: An empirical study was carried out for test suite generation on a series of artificial examples and seven RESTful API web services. The \evo system test generation tool was used, where MIO, MOSA, WTS and random search were compared. Results: The presented MIO algorithm resulted having the best overall performance, but was not the best on all problems. Conclusion: The novel presented MIO algorithm is a step forward in the automation of test suite generation for system testing. However, there are still properties of system testing that can be exploited to achieve even better results. [less ▲]

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See detailRandom or Evolutionary Search for Object-Oriented Test Suite Generation?
Shamshiri; Rojas; Gazzola et al

in Software Testing, Verification & Reliability (in press)

An important aim in software testing is constructing a test suite with high structural code coverage – that is, ensuring that most if not all of the code under test has been executed by the test cases ... [more ▼]

An important aim in software testing is constructing a test suite with high structural code coverage – that is, ensuring that most if not all of the code under test has been executed by the test cases comprising the test suite. Several search-based techniques have proved successful at automatically generating tests that achieve high coverage. However, despite the well-established arguments behind using evolutionary search algorithms (e.g., genetic algorithms) in preference to random search, it remains an open question whether the benefits can actually be observed in practice when generating unit test suites for object-oriented classes. In this paper, we report an empirical study on the effects of using evolutionary algorithms (including a genetic algorithm and chemical reaction optimization) to generate test suites, compared with generating test suites incrementally with random search. We apply the EVOSUITE unit test suite generator to 1,000 classes randomly selected from the SF110 corpus of open source projects. Surprisingly, the results show that the difference is much smaller than one might expect: While evolutionary search covers more branches of the type where standard fitness functions provide guidance, we observed that, in practice, the vast majority of branches do not provide any guidance to the search. These results suggest that, although evolutionary algorithms are more effective at covering complex branches, a random search may suffice to achieve high coverage of most object-oriented classes. [less ▲]

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