Identification of Software Features in Issue Tracking System Data

The knowledge of Software Features (SFs) is vital for software developers and requirements specialists during all software engineering phases: to understand and derive software requirements, to plan and prioritize implementation tasks, to update documentation, or to test whether the final product correctly implements the requested SF. In most software projects, SFs are managed in conjunction with other information such as bug reports, programming tasks, or refactoring tasks with the aid of Issue Tracking Systems (ITSs). Hence ITSs contains a variety of information that is only partly related to SFs. In practice, however, the usage of ITSs to store SFs comes with two major problems: (1) ITSs are neither designed nor used as documentation systems. Therefore, the data inside an ITS is often uncategorized and SF descriptions are concealed in rather lengthy. (2) Although an SF is often requested in a single sentence, related information can be scattered among many issues. E.g. implementation tasks related to an SF are often reported in additional issues. Hence, the detection of SFs in ITSs is complicated: a manual search for the SFs implies reading, understanding and exploiting the Natural Language (NL) in many issues in detail. This is cumbersome and labor intensive, especially if related information is spread over more than one issue. This thesis investigates whether SF detection can be supported automatically. First the problem is analyzed: (i) An empirical study shows that requests for important SFs reside in ITSs, making ITSs a good tar- get for SF detection. (ii) A second study identifies characteristics of the information and related NL in issues. These characteristics repre- sent opportunities as well as challenges for the automatic detection of SFs. Based on these problem studies, the Issue Tracking Software Feature Detection Method (ITSoFD), is proposed. The method has two main components and includes an approach to preprocess issues. Both components address one of the problems associated with storing SFs in ITSs. ITSoFD is validated in three solution studies: (I) An empirical study researches how NL that describes SFs can be detected with techniques from Natural Language Processing (NLP) and Machine Learning. Issues are parsed and different characteristics of the issue and its NL are extracted. These characteristics are used to clas- sify the issue’s content and identify SF description candidates, thereby approaching problem (1). (II) An empirical study researches how issues that carry information potentially related to an SF can be detected with techniques from NLP and Information Retrieval. Characteristics of the issue’s NL are utilized to create a traceability network vii of related issues, thereby approaching problem (2). (III) An empirical study researches how NL data in issues can be preprocessed using heuristics and hierarchical clustering. Code, stack traces, and other technical information is separated from NL. Heuristics are used to identify candidates for technical information and clustering improves the heuristic’s results. The technique can be applied to support components, I. and II

A novel metabarcoding primer pair for environmental DNA analysis of Cephalopoda (Mollusca) targeting the nuclear 18S rRNA region

Cephalopods are pivotal components of marine food webs, but biodiversity studies are hampered by challenges to sample these agile marine molluscs. Metabarcoding of environmental DNA (eDNA) is a potentially powerful technique to study oceanic cephalopod biodiversity and distribution but has not been applied thus far. We present a novel universal primer pair for metabarcoding cephalopods from eDNA, Ceph18S (Forward: 5′-CGC GGC GCT ACA TAT TAG AC-3′, Reverse: 5′-GCA CTT AAC CGA CCG TCG AC-3′). The primer pair targets the hypervariable region V2 of the nuclear 18S rRNA gene and amplifies a relatively short target sequence of approximately 200 bp in order to allow the amplification of degraded DNA. In silico tests on a reference database and empirical tests on DNA extracts from cephalopod tissue estimate that 44-66% of cephalopod species, corresponding to about 310-460 species, can be amplified and identified with this primer pair. A multi-marker approach with the novel Ceph18S and two previously published cephalopod mitochondrial 16S rRNA primer sets targeting the same region (Jarman et al. 2006 Mol. Ecol. Notes. 6, 268-271; Peters et al. 2015 Mar. Ecol. 36, 1428-1439) is estimated to amplify and identify 89% of all cephalopod species, of which an estimated 19% can only be identified by Ceph18S. All sequences obtained with Ceph18S were submitted to GenBank, resulting in new 18S rRNA sequences for 13 cephalopod tax

Deep-sea predator niche segregation revealed by combined cetacean biologging and eDNA analysis of cephalopod prey

Fundamental insight on predator-prey dynamics in the deep sea is hampered by a lack of combined data on hunting behavior and prey spectra. Deep-sea niche segregation may evolve when predators target specific prey communities, but this hypothesis remains untested. We combined environmental DNA (eDNA) metabarcoding with biologging to assess cephalopod community composition in the deep-sea foraging habitat of two top predator cetaceans. Risso’s dolphin and Cuvier’s beaked whale selectively targeted distinct epi/meso- and bathypelagic foraging zones, holding eDNA of 39 cephalopod taxa, including 22 known prey. Contrary to expectation, extensive taxonomic overlap in prey spectra between foraging zones indicated that predator niche segregation was not driven by prey community composition alone. Instead, intraspecific prey spectrum differences may drive differentiation for hunting fewer, more calorific, mature cephalopods in deeper waters. The novel combination of methods presented here holds great promise to disclose elusive deep-sea predator-prey systems, aiding in their protection

Silver‐Catalyzed Enantioselective Sulfimidation Mediated by Hydrogen Bonding Interactions

An enantioselective sulfimidation of 3-thiosubstituted 2-quinolones and 2-pyridones was achieved with a stoichiometric nitrene source (PhI=NNs) and a silver-based catalyst system. Key to the success of the reaction is the use of a chiral phenanthroline ligand with a hydrogen bonding site. The enantioselectivity does not depend on the size of the two substituents at the sulfur atom but only on the binding properties of the heterocyclic lactams. A total of 21 chiral sulfimides were obtained in high yields (44-99 %) and with significant enantiomeric excess (70-99 % ee). The sulfimidation proceeds with high site-selectivity and can also be employed for the kinetic resolution of chiral sulfoxides. Mechanistic evidence suggests the intermediacy of a heteroleptic silver complex, in which the silver atom is bound to one molecule of the chiral ligand and one molecule of an achiral 1,10-phenanthroline. Support for the suggested reaction course was obtained by ESI mass spectrometry, DFT calculations, and a Hammett analysis

Galactic Core-Collapse Supernovae at IceCube: “Fire Drill” Data Challenges and follow-up

The next Galactic core-collapse supernova (CCSN) presents a once-in-a-lifetime opportunity to make astrophysical measurements using neutrinos, gravitational waves, and electromagnetic radiation. CCSNe local to the Milky Way are extremely rare, so it is paramount that detectors are prepared to observe the signal when it arrives. The IceCube Neutrino Observatory, a gigaton water Cherenkov detector below the South Pole, is sensitive to the burst of neutrinos released by a Galactic CCSN at a level >10σ. This burst of neutrinos precedes optical emission by hours to days, enabling neutrinos to serve as an early warning for follow-up observation. IceCube\u27s detection capabilities make it a cornerstone of the global network of neutrino detectors monitoring for Galactic CCSNe, the SuperNova Early Warning System (SNEWS 2.0). In this contribution, we describe IceCube\u27s sensitivity to Galactic CCSNe and strategies for operational readiness, including "fire drill" data challenges. We also discuss coordination with SNEWS 2.0

All-Energy Search for Solar Atmospheric Neutrinos with IceCube

The interaction of cosmic rays with the solar atmosphere generates a secondary flux of mesons that decay into photons and neutrinos – the so-called solar atmospheric flux. Although the gamma-ray component of this flux has been observed in Fermi-LAT and HAWC Observatory data, the neutrino component remains undetected. The energy distribution of those neutrinos follows a soft spectrum that extends from the GeV to the multi-TeV range, making large Cherenkov neutrino telescopes a suitable for probing this flux. In this contribution, we will discuss current progress of a search for the solar neutrino flux by the IceCube Neutrino Observatory using all available data since 2011. Compared to the previous analysis which considered only high-energy muon neutrino tracks, we will additionally consider events produced by all flavors of neutrinos down to GeV-scale energies. These new events should improve our analysis sensitivity since the flux falls quickly with energy. Determining the magnitude of the neutrino flux is essential, since it is an irreducible background to indirect solar dark matter searches

TXS 0506+056 with Updated IceCube Data

Past results from the IceCube Collaboration have suggested that the blazar TXS 0506+056 is a potential source of astrophysical neutrinos. However, in the years since there have been numerous updates to event processing and reconstruction, as well as improvements to the statistical methods used to search for astrophysical neutrino sources. These improvements in combination with additional years of data have resulted in the identification of NGC 1068 as a second neutrino source candidate. This talk will re-examine time-dependent neutrino emission from TXS 0506+056 using the most recent northern-sky data sample that was used in the analysis of NGC 1068. The results of using this updated data sample to obtain a significance and flux fit for the 2014 TXS 0506+056 "untriggered" neutrino flare are reported

Recent neutrino oscillation results with the IceCube experiment

The IceCube South Pole Neutrino Observatory is a Cherenkov detector instrumented in a cubic kilometer of ice at the South Pole. IceCube’s primary scientific goal is the detection of TeV neutrino emissions from astrophysical sources. At the lower center of the IceCube array, there is a subdetector called DeepCore, which has a denser configuration that makes it possible to lower the energy threshold of IceCube and observe GeV-scale neutrinos, opening the window to atmospheric neutrino oscillations studies. Advances in physics sensitivity have recently been achieved by employing Convolutional Neural Networks to reconstruct neutrino interactions in the DeepCore detector. In this contribution, the recent IceCube result from the atmospheric muon neutrino disappearance analysis using the CNN-reconstructed neutrino sample are presented and compared to the existing worldwide measurements

Angular dependence of the atmospheric neutrino flux with IceCube data

IceCube Neutrino Observatory, the cubic kilometer detector embedded in ice of the geographic South Pole, is capable of detecting particles from several GeV up to PeV energies enabling precise neutrino spectrum measurement. The diffuse neutrino flux can be subdivided into three components: astrophysical, from extraterrestrial sources; conventional, from pion and kaon decays in atmospheric Cosmic Ray cascades; and the yet undetected prompt component from the decay of charmed hadrons. A particular focus of this work is to test the predicted angular dependence of the atmospheric neutrino flux using an unfolding method. Unfolding is a set of methods aimed at determining a value from related quantities in a model-independent way, eliminating the influence of several assumptions made in the process. In this work, we unfold the muon neutrino energy spectrum and employ a novel technique for rebinning the observable space to ensure sufficient event numbers within the low statistic region at the highest energies. We present the unfolded energy and zenith angle spectrum reconstructed from IceCube data and compare the result with model expectations and previous measurements