Empirical Evaluation of Semi-automated XML Annotation of Text Documents with the GoldenGATE Editor

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Eine Architektur für Programmsynthese aus natürlicher Sprache

Diese Arbeit beschreibt, wie man natürlichsprachliche Schnittstellen für die Programmierung von Software-Anwendungen erstellen kann. Hierbei wird insbesondere betrachtet, wie die Textanalyse vom konkreten Einsatzszenario, d.h. der angesteuerten Software, entkoppelt und somit wiederverwendbar gemacht werden kann. Zur Evaluation werden zwei Fallstudien vorgestellt: Die befehlsgesteuerte Heimautomatisierung mit openHAB und die Erzeugung von 3D-Animationen aus englischen Drehbüchern mit Alice

Eine Architektur für Programmsynthese aus natürlicher Sprache

Die Arbeit entwirft ein domänenunabhängiges Vorgehen, das aus natürlichsprachlichen englischen Texten Programme ableitet. Eingabetexte werden mit computerlinguistischen Analysen bearbeitet und schließlich auf Quelltext abgebildet. Um die Analysen von der anzusteuernden API zu entkoppeln, wird diese als Modell in den Analyseprozess eingespeist. Eine Fallstudie zur Programmierung von 3D-Animationen und eine zur Heimautomation zeigen die Leistungsfähigkeit und Flexibilität des Verfahrens

Does Automated Unit Test Generation Really Help Software Testers? A Controlled Empirical Study

Work on automated test generation has produced several tools capable of generating test data which achieves high structural coverage over a program. In the absence of a specification, developers are expected to manually construct or verify the test oracle for each test input. Nevertheless, it is assumed that these generated tests ease the task of testing for the developer, as testing is reduced to checking the results of tests. While this assumption has persisted for decades, there has been no conclusive evidence to date confirming it. However, the limited adoption in industry indicates this assumption may not be correct, and calls into question the practical value of test generation tools. To investigate this issue, we performed two controlled experiments comparing a total of 97 subjects split between writing tests manually and writing tests with the aid of an automated unit test generation tool, EvoSuite. We found that, on one hand, tool support leads to clear improvements in commonly applied quality metrics such as code coverage (up to 300% increase). However, on the other hand, there was no measurable improvement in the number of bugs actually found by developers. Our results not only cast some doubt on how the research community evaluates test generation tools, but also point to improvements and future work necessary before automated test generation tools will be widely adopted by practitioners

Diagnostics in Plant Breeding

“Diagnostics in Plant Breeding” is systematically organizing cutting-edge research reviews on the development and application of molecular tools for the prediction of plant performance. Given its significance for mankind and the available research resources, medical sciences are leading the area of molecular diagnostics, where DNA-based risk assessments for various diseases and biomarkers to determine their onset become increasingly available. So far, most research in plant genomics has been directed towards understanding the molecular basis of biological processes or phenotypic traits. From a plant breeding perspective, however, the main interest is in predicting optimal genotypes based on molecular information for more time- and cost-efficient breeding schemes. It is anticipated that progress in plant genomics and in particular sequence technology made recently will shift the focus from “explanatory” to “predictive” in crop science. This book assembles chapters on all areas relevant to development and application of predictive molecular tools in plant breeding by leading authorties in the respective areas