My latest contributions to software engineering research

A Critical Evaluation of Spectrum-Based Fault Localization Techniques on a Large-Scale Software System

This paper is published in Software Quality, Reliability and Security (QRS) 2017. It is a paper on the topic of my bachelor’s thesis. Abstract In the past, spectrum-based fault localization (SBFL) techniques have been developed to pinpoint a fault location in a program given a set of failing and successful test executions. Most of the algorithms use similarity coefficients and have only been evaluated on established but small benchmark programs from the Software-artifact Infrastructure Repository (SIR).

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Master's Thesis: Introducing Performance Awareness in an Integrated Specification Environment

Abstract With an increase in software complexity and modularization to create large software systems and software product lines it is increasingly difficult to ensure all requirements are met by the built system. Performance requirements are an important concern to software systems and research has developed approaches being capable of predicting software performance from annotated software architecture descriptions, such as the Palladio tool suite. However, the tooling when moving between specification, implementation and verification phase has a gap as the tools are commonly not linked, leading to inconsistencies and ambiguities in the produced artifacts.

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Leveraging Palladio for Performance Awareness in the IETS3 Integrated Specification Environment

This paper is published in the Symposium on Software Performance 2016. It is a short paper on the topic of my master’s thesis. Abstract Performance is an important concern when designing and implementing software-intensive systems. Various techniques are available for specifying and evaluating performance concerns throughout the system life-cycle. However, there is a gap in terms of tooling when moving between requirements, design, and implementation artifacts. We address this gap by integrating simulation-based and analytical performance prediction tools into IETS3 - an integrated specifi cation environment for technical software systems based on the JetBrains MPS language workbench.

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High Dimensional Data Visualization

I attended a seminar on large scale visualization and wrote a paper on high dimensional data visualization, comparing different techniques. A key concept of high dimensional data visualization is dimensionality reduction, in order to reduce the number of dimensions to be visualized. The paper covers linear (principal component analysis) and non-linear (local linear embedding, ISOMAP, t-SNE) dimensionality reduction techniques. The paper has been peer-reviewed in class. The title photo is a visualization of the MNIST dataset - a database of handwritten digits.

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