A Fault Localization and Debugging Support Framework driven by Bug Tracking Data

Fault localization has been determined as a major resource factor in the software development life cycle. Academic fault localization techniques are mostly unknown and unused in professional environments. Although manual debugging approaches can vary significantly depending on bug type (e.g. memory bugs or semantic bugs), these differences are not reflected in most existing fault localization tools. Little research has gone into automated identification of bug types to optimize the fault localization process. Further, existing fault localization techniques leverage on historical data only for augmentation of suspiciousness rankings. This thesis aims to provide a fault localization framework by combining data from various sources to help developers in the fault localization process. To achieve this, a bug classification schema is introduced, benchmarks are created, and a novel fault localization method based on historical data is proposed.Comment: 4 page

Project--Disciple-A Model for Parish Youth Ministry

This study is written to assist youth leaders and adult counselors in working out models for ministry among young people in the parish. As this model develops it will become obvious that its practical application is limited to the parish medium, although many of the principles underlying its structure may indeed find adaptation elsewhere. To further aid the reader, a rather extensive bibliography is included also. With such an index of available resources, the creative youth leader or adult counselor will have an excellent platform from which to begin youth ministry

Root cause prediction based on bug reports

This paper proposes a supervised machine learning approach for predicting the root cause of a given bug report. Knowing the root cause of a bug can help developers in the debugging process - either directly or indirectly by choosing proper tool support for the debugging task. We mined 54755 closed bug reports from the issue trackers of 103 GitHub projects and applied a set of heuristics to create a benchmark consisting of 10459 reports. A subset was manually classified into three groups (semantic, memory, and concurrency) based on the bugs' root causes. Since the types of root cause are not equally distributed, a combination of keyword search and random selection was applied. Our data set for the machine learning approach consists of 369 bug reports (122 concurrency, 121 memory, and 126 semantic bugs). The bug reports are used as input to a natural language processing algorithm. We evaluated the performance of several classifiers for predicting the root causes for the given bug reports. Linear Support Vector machines achieved the highest mean precision (0.74) and recall (0.72) scores. The created bug data set and classification are publicly available.Comment: 6 page

Current Trends in the Optical Characterization of Two-Dimensional Carbon Nanomaterials

Graphene and graphene-related materials have received great attention because of their outstanding properties like Young's modulus, chemical inertness, high electrical and thermal conductivity, or large mobility. To utilize two-dimensional (2D) materials in any practical application, an excellent characterization of the nanomaterials is needed as such dimensions, even small variations in size, or composition, are accompanied by drastic changes in the material properties. Simultaneously, it is sophisticated to perform characterizations at such small dimensions. This review highlights the wide range of different characterization methods for the 2D materials, mainly attributing carbon-based materials as they are by far the ones most often used today. The strengths as well as the limitations of the individual methods, ranging from light microscopy, scanning electron microscopy, transmission electron microscopy, scanning transmission electron microscopy, scanning tunneling microscopy (conductive), atomic force microscopy, scanning electrochemical microscopy, Raman spectroscopy, UV-vis, X-ray photoelectron spectroscopy, X-ray fluorescence spectroscopy, energy-dispersive X-ray spectroscopy, Auger electron spectroscopy, electron energy loss spectroscopy, X-ray diffraction, inductively coupled plasma atomic emission spectroscopy to dynamic light scattering, are discussed. By using these methods, the flake size and shape, the number of layers, the conductivity, the morphology, the number and type of defects, the chemical composition, and the colloidal properties of the 2D materials can be investigated

Long-term Colloidal and Chemical Stability in Aqueous Media of NaYF₄-type Upconversion Nanoparticles Modified by Ligand-Exchange

Surface capping is an essential component of nanoparticles as it provides access to their outstanding properties in the real world. Upconversion nanoparticles are predominantly interesting for use in biological environments, due to their excellent optical properties such as the conversion of near-infrared excitation light into emissions in the visible or UV range of the spectrum, high photostability, and the absence of any intermittence. One of the most efficient upconversion nanoparticles, consisting of lanthanide doped NaYF4, suffers from limited stability in aqueous media. This study investigates a set of five types of surface coatings, ranging from small ligands to polymers of different charge and different coordinating groups, on monodisperse 28 +/- 0.9 nm sized NaYF4(Yb,Er) nanoparticles modified by a two-step ligand exchange mediated by NOBF4. Information on the long-term chemical and colloidal stability for highly diluted aqueous dispersions of these particles is acquired by transmission electron microscopy, dynamic light scattering, and luminescence spectroscopy. The findings are of importance for the development of probes and labels based on upconversion nanoparticles for biological applications

Process-Based Management Approaches for Salt Desert Shrublands Dominated by Downy Brome

Downy brome grass (Bromus tectorum L.) invasion has severely altered key ecological processes such as disturbance regimes, soil nutrient cycling, community assembly, and successional pathways in semi- arid Great Basin salt desert shrublands. Restoring the structure and function of these severely altered ecosystems is extremely challenging; however new strategies are emerging that target and attempt to repair ecological processes associated with vegetation change. In this paper, we review the essential processes required to reduce downy brome abundance and assist with creating suitable conditions for revegetation of Great Basin salt desert shrublands

The effect of negative feedback loops on the dynamics of Boolean networks

Feedback loops in a dynamic network play an important role in determining the dynamics of that network. Through a computational study, in this paper we show that networks with fewer independent negative feedback loops tend to exhibit more regular behavior than those with more negative loops. To be precise, we study the relationship between the number of independent feedback loops and the number and length of the limit cycles in the phase space of dynamic Boolean networks. We show that, as the number of independent negative feedback loops increases, the number (length) of limit cycles tends to decrease (increase). These conclusions are consistent with the fact, for certain natural biological networks, that they on the one hand exhibit generally regular behavior and on the other hand show less negative feedback loops than randomized networks with the same numbers of nodes and connectivity

Determining the temporal direction of psychological distress and substance use in female expatriate spouses in Turkey

Expatriation has been associated with a number of negative mental health issues within the expats themselves (e.g., depression, substance use). However, expatriate spouses can often face unique stressors, and by gaining a better understanding of the impact expatriation has on these spouses, organizations can more easily plan for the challenges. The purpose of our study is to further explore the direction of the relationship between alcohol use and psychological distress in a sample of female expatriate spouses during their first year assignment in Turkey. By using hierarchical linear modeling and conducting a cross-lead analysis, we will analyze the change over time for alcohol use and psychological distress

Determining R-parity violating parameters from neutrino and LHC data

In supersymmetric models neutrino data can be explained by R-parity violating operators which violate lepton number by one unit. The so called bilinear model can account for the observed neutrino data and predicts at the same time several decay properties of the lightest supersymmetric particle. In this paper we discuss the expected precision to determine these parameters by combining neutrino and LHC data and discuss the most important observables. We show that one can expect a rather accurate determination of the underlying R-parity parameters assuming mSUGRA relations between the R-parity conserving ones and discuss briefly also the general MSSM as well as the expected accuracies in case of a prospective e+ e- linear collider. An important observation is that several parameters can only be determined up to relative signs or more generally relative phases.Comment: 13 pages, 13 figure