The Experts below are selected from a list of 27111 Experts worldwide ranked by ideXlab platform
Armin Beer - One of the best experts on this subject based on the ideXlab platform.
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on the role of defect taxonomy types for testing requirements results of a controlled experiment
Software Engineering and Advanced Applications, 2014Co-Authors: Michael Felderer, Armin Beer, Bernhard PeischlAbstract:Context: The use of defect Taxonomies and their assignment to requirements can improve system-level testing of requirements. Experiences from industrial applications indicate that the type of the underlying top-level defect categories constitutes the main influence factor for defining defect Taxonomies. Objective: The objective addressed in this paper is to investigate the influence of the type of top-level defect categories on the quality of the created defect taxonomy, on the quality of the assignment of requirements to defect categories as well as the quality of designed test cases. Method: We conducted a controlled student experiment to determine the influence of two different types of top-level defect categories, i.e., Generic and web application specific, on the quality of the created defect taxonomy, their assignment to requirements and the derived test cases. Results: The results indicate an influence of the type of the top-level defect categories on the quality of the derived defect taxonomy but no influence on the quality of the assignment of requirements to defect categories and the quality of the designed test cases. Contribution: The main contributions of this paper is the empirical investigation of the role of the type of top-level defect Taxonomies themselves for testing requirements and the consequences of the results for industrial application.
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Using defect Taxonomies for requirements validation in industrial projects
2013 21st IEEE International Requirements Engineering Conference (RE), 2013Co-Authors: Michael Felderer, Armin BeerAbstract:Quality of requirements is of great importance for the software development lifecycle as it influences all steps of software development. To ensure various quality attributes, suitable requirements validation techniques such as reviews or testing are essential. In this paper, we show how defect Taxonomies can improve requirements reviews and testing. We point out how defect Taxonomies can be seamlessly integrated into the requirements engineering process and discuss requirements validation with defect Taxonomies as well as its benefits and the lessons learned with reference to industrial projects of a public health insurance institution where this approach has been successfully applied.
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using defect Taxonomies to improve the maturity of the system test process results from an industrial case study
International Conference on Software Quality, 2013Co-Authors: Michael Felderer, Armin BeerAbstract:Defect Taxonomies collect and organize the domain knowledge and project experience of experts and are a valuable instrument of system testing for several reasons. They provide systematic backup for the design of tests, support decisions for the allocation of testing resources and are a suitable basis for measuring the product and test quality. In this paper, we propose a method of system testing based on defect Taxonomies and investigate how these can systematically improve the efficiency and effectiveness, i.e. the maturity of requirements-based testing. The method is evaluated via an industrial case study based on two projects from a public health insurance institution by comparing one project with defect taxonomy-supported testing and one without. Empirical data confirm that system testing supported by defect Taxonomies (1) reduces the number of test cases, and (2) increases of the number of identified failures per test case.
Frederik Hogenboom - One of the best experts on this subject based on the ideXlab platform.
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domain taxonomy learning from text the subsumption method versus hierarchical clustering
Data and Knowledge Engineering, 2013Co-Authors: Jeroen De Knijff, Flavius Frasincar, Frederik HogenboomAbstract:This paper proposes a framework to automatically construct Taxonomies from a corpus of text documents. This framework first extracts terms from documents using a part-of-speech parser. These terms are then filtered using domain pertinence, domain consensus, lexical cohesion, and structural relevance. The remaining terms represent concepts in the taxonomy. These concepts are arranged in a hierarchy with either the extended subsumption method that accounts for concept ancestors in determining the parent of a concept or a hierarchical clustering algorithm that uses various text-based window and document scopes for concept co-occurrences. Our evaluation in the field of management and economics indicates that a trade-off between taxonomy quality and depth must be made when choosing one of these methods. The subsumption method is preferable for shallow Taxonomies, whereas the hierarchical clustering algorithm is recommended for deep Taxonomies.
Gerhard Weikum - One of the best experts on this subject based on the ideXlab platform.
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multi cultural interlinking of web Taxonomies with across
Web Science, 2018Co-Authors: Natalia Boldyrev, Marc Spaniol, Gerhard WeikumAbstract:The Web hosts a huge variety of multi-cultural Taxonomies. They encompass product catalogs of e-commerce, general-purpose knowledge bases and numerous domain-specific category systems. The enormous heterogeneity of those sources is a challenging aspect when multiple Taxonomies have to be interlinked. In this paper we introduce ACROSS system to support the alignment of independently created Web Taxonomies. For mapping categories across different Taxonomies, ACROSS harnesses instance-level features as well as distant supervision from an intermediate source like multiple Wikipedia editions. ACROSS includes a reasoning step, which is based on combinatorial optimization. In order to reduce the run time of the reasoning procedure without sacrificing the quality, we study two models of user involvement. Our experiments with heterogeneous Taxonomies for different domains demonstrate the viability of our approach and improvement over state-of-the-art baselines.
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ACROSS: A framework for multi-cultural interlinking of web Taxonomies
2016Co-Authors: Natalia Boldyrev, Marc Spaniol, Gerhard WeikumAbstract:The Web hosts a huge variety of multi-cultural Taxonomies. They encompass product catalogs of e-commerce, generalpurpose knowledge bases and numerous domain-specific category systems. The "common denominator" of all these is their enormous diversity, which makes it infeasible to combine multiple Taxonomies for ad-hoc tasks. To support the alignment of independently created Web Taxonomies, we introduce the ACROSS framework. For mapping categories across different Taxonomies, ACROSS harnesses instance-level features as well as distant supervision from an intermediate source like multiple Wikipedia editions. Our experiments with heterogeneous Taxonomies for different domains demonstrate the viability of our approach and improvement over state-of-theart baselines.
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menta inducing multilingual Taxonomies from wikipedia
Conference on Information and Knowledge Management, 2010Co-Authors: Gerard De Melo, Gerhard WeikumAbstract:In recent years, a number of projects have turned to Wikipedia to establish large-scale Taxonomies that describe orders of magnitude more entities than traditional manually built knowledge bases. So far, however, the multilingual nature of Wikipedia has largely been neglected. This paper investigates how entities from all editions of Wikipedia as well as WordNet can be integrated into a single coherent taxonomic class hierarchy. We rely on linking heuristics to discover potential taxonomic relationships, graph partitioning to form consistent equivalence classes of entities, and a Markov chain-based ranking approach to construct the final taxonomy. This results in MENTA (Multilingual Entity Taxonomy), a resource that describes 5.4 million entities and is presumably the largest multilingual lexical knowledge base currently available.
Michael Felderer - One of the best experts on this subject based on the ideXlab platform.
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on the role of defect taxonomy types for testing requirements results of a controlled experiment
Software Engineering and Advanced Applications, 2014Co-Authors: Michael Felderer, Armin Beer, Bernhard PeischlAbstract:Context: The use of defect Taxonomies and their assignment to requirements can improve system-level testing of requirements. Experiences from industrial applications indicate that the type of the underlying top-level defect categories constitutes the main influence factor for defining defect Taxonomies. Objective: The objective addressed in this paper is to investigate the influence of the type of top-level defect categories on the quality of the created defect taxonomy, on the quality of the assignment of requirements to defect categories as well as the quality of designed test cases. Method: We conducted a controlled student experiment to determine the influence of two different types of top-level defect categories, i.e., Generic and web application specific, on the quality of the created defect taxonomy, their assignment to requirements and the derived test cases. Results: The results indicate an influence of the type of the top-level defect categories on the quality of the derived defect taxonomy but no influence on the quality of the assignment of requirements to defect categories and the quality of the designed test cases. Contribution: The main contributions of this paper is the empirical investigation of the role of the type of top-level defect Taxonomies themselves for testing requirements and the consequences of the results for industrial application.
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Using defect Taxonomies for requirements validation in industrial projects
2013 21st IEEE International Requirements Engineering Conference (RE), 2013Co-Authors: Michael Felderer, Armin BeerAbstract:Quality of requirements is of great importance for the software development lifecycle as it influences all steps of software development. To ensure various quality attributes, suitable requirements validation techniques such as reviews or testing are essential. In this paper, we show how defect Taxonomies can improve requirements reviews and testing. We point out how defect Taxonomies can be seamlessly integrated into the requirements engineering process and discuss requirements validation with defect Taxonomies as well as its benefits and the lessons learned with reference to industrial projects of a public health insurance institution where this approach has been successfully applied.
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using defect Taxonomies to improve the maturity of the system test process results from an industrial case study
International Conference on Software Quality, 2013Co-Authors: Michael Felderer, Armin BeerAbstract:Defect Taxonomies collect and organize the domain knowledge and project experience of experts and are a valuable instrument of system testing for several reasons. They provide systematic backup for the design of tests, support decisions for the allocation of testing resources and are a suitable basis for measuring the product and test quality. In this paper, we propose a method of system testing based on defect Taxonomies and investigate how these can systematically improve the efficiency and effectiveness, i.e. the maturity of requirements-based testing. The method is evaluated via an industrial case study based on two projects from a public health insurance institution by comparing one project with defect taxonomy-supported testing and one without. Empirical data confirm that system testing supported by defect Taxonomies (1) reduces the number of test cases, and (2) increases of the number of identified failures per test case.
Dan Klein - One of the best experts on this subject based on the ideXlab platform.
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constructing Taxonomies from pretrained language models
North American Chapter of the Association for Computational Linguistics, 2021Co-Authors: Catherine L Chen, Kevin Lin, Dan KleinAbstract:We present a method for constructing taxonomic trees (e.g., WordNet) using pretrained language models. Our approach is composed of two modules, one that predicts parenthood relations and another that reconciles those pairwise predictions into trees. The parenthood prediction module produces likelihood scores for each potential parent-child pair, creating a graph of parent-child relation scores. The tree reconciliation module treats the task as a graph optimization problem and outputs the maximum spanning tree of this graph. We train our model on subtrees sampled from WordNet, and test on nonoverlapping WordNet subtrees. We show that incorporating web-retrieved glosses can further improve performance. On the task of constructing subtrees of English WordNet, the model achieves 66.7 ancestor F1, a 20.0% relative increase over the previous best published result on this task. In addition, we convert the original English dataset into nine other languages using Open Multilingual WordNet and extend our results across these languages.
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constructing Taxonomies from pretrained language models
arXiv: Computation and Language, 2020Co-Authors: Catherine L Chen, Kevin Lin, Dan KleinAbstract:We present a method for constructing taxonomic trees (e.g., WordNet) using pretrained language models. Our approach is composed of two modules, one that predicts parenthood relations and another that reconciles those predictions into trees. The parenthood prediction module produces likelihood scores for each potential parent-child pair, creating a graph of parent-child relation scores. The tree reconciliation module treats the task as a graph optimization problem and outputs the maximum spanning tree of this graph. We train our model on subtrees sampled from WordNet, and test on non-overlapping WordNet subtrees. We show that incorporating web-retrieved glosses can further improve performance. On the task of constructing subtrees of English WordNet, the model achieves 66.7 ancestor F1, a 20.0% relative increase over the previous best published result on this task. In addition, we convert the original English dataset into nine other languages using Open Multilingual WordNet and extend our results across these languages.
