VLBI study of maser kinematics in high-mass SFRs. II. G23.01-0.41

The present paper focuses on the high-mass star-forming region G23.01-0.41. Methods: Using the VLBA and the EVN arrays, we conducted phase-referenced observations of the three most powerful maser species in G23.01-0.41: H2O at 22.2 GHz (4 epochs), CH3OH at 6.7 GHz (3 epochs), and OH at 1.665 GHz (1 epoch). In addition, we performed high-resolution (> 0".1), high-sensitivity (< 0.1 mJy) VLA observations of the radio continuum emission from the HMC at 1.3 and 3.6 cm. Results: We have detected H2O, CH3OH, and OH maser emission clustered within 2000 AU from the center of a flattened HMC, oriented SE-NW, from which emerges a massive 12CO outflow, elongated NE-SW, extended up to the pc-scale. Although the three maser species show a clearly different spatial and velocity distribution and sample distinct environments around the massive YSO, the spatial symmetry and velocity field of each maser specie can be explained in terms of expansion from a common center, which possibly denotes the position of the YSO driving the maser motion. Water masers trace both a fast shock (up to 50 km/s) closer to the YSO, powered by a wide-angle wind, and a slower (20 km/s) bipolar jet, at the base of the large-scale outflow. Since the compact free-free emission is found offset from the putative location of the YSO along a direction consistent with that of the maser jet axis, we interpret the radio continuum in terms of a thermal jet. The velocity field of methanol masers can be explained in terms of a composition of slow (4 km/s in amplitude) motions of radial expansion and rotation about an axis approximately parallel to the maser jet. Finally, the distribution of line of sight velocities of the hydroxyl masers suggests that they can trace gas less dense (n(H2) < 10^6 cm^-3) and more distant from the YSO than that traced by the water and methanol masers, which is expanding toward the observer. (Abridged)Comment: 23 pages, 8 figures, 4 tables, accepted by Astronomy and Astrophysic

Early aspects: aspect-oriented requirements engineering and architecture design

This paper reports on the third Early Aspects: Aspect-Oriented Requirements Engineering and Architecture Design Workshop, which has been held in Lancaster, UK, on March 21, 2004. The workshop included a presentation session and working sessions in which the particular topics on early aspects were discussed. The primary goal of the workshop was to focus on challenges to defining methodical software development processes for aspects from early on in the software life cycle and explore the potential of proposed methods and techniques to scale up to industrial applications

Leveraging Intermediate Artifacts to Improve Automated Trace Link Retrieval

Software traceability establishes a network of connections between diverse artifacts such as requirements, design, and code. However, given the cost and effort of creating and maintaining trace links manually, researchers have proposed automated approaches using information retrieval techniques. Current approaches focus almost entirely upon generating links between pairs of artifacts and have not leveraged the broader network of interconnected artifacts. In this paper we investigate the use of intermediate artifacts to enhance the accuracy of the generated trace links – focus- ing on paths consisting of source, target, and intermediate artifacts. We propose and evaluate combinations of techniques for computing semantic similarity, scaling scores across multiple paths, and aggregating results from multiple paths. We report results from five projects, including one large industrial project. We find that leverag- ing intermediate artifacts improves the accuracy of end-to-end trace retrieval across all datasets and accuracy metrics. After further analysis, we discover that leveraging intermediate artifacts is only helpful when a project’s artifacts share a common vocabulary, which tends to occur in refinement and decomposition hierarchies of artifacts. Given our hybrid approach that integrates both direct and transitive links, we observed little to no loss of accuracy when intermediate artifacts lacked a shared vocabulary with source or target artifacts

Calibration of the NuSTAR High Energy Focusing X-ray Telescope

We present the calibration of the \textit{Nuclear Spectroscopic Telescope Array} (\nustar) X-ray satellite. We used the Crab as the primary effective area calibrator and constructed a piece-wise linear spline function to modify the vignetting response. The achieved residuals for all off-axis angles and energies, compared to the assumed spectrum, are typically better than $\pm 2$\% up to 40\,keV and 5--10\,\% above due to limited counting statistics. An empirical adjustment to the theoretical 2D point spread function (PSF) was found using several strong point sources, and no increase of the PSF half power diameter (HPD) has been observed since the beginning of the mission. We report on the detector gain calibration, good to 60\,eV for all grades, and discuss the timing capabilities of the observatory, which has an absolute timing of $\pm$ 3\,ms. Finally we present cross-calibration results from two campaigns between all the major concurrent X-ray observatories (\textit{Chandra}, \textit{Swift}, \textit{Suzaku} and \textit{XMM-Newton}), conducted in 2012 and 2013 on the sources 3C\,273 and PKS\,2155-304, and show that the differences in measured flux is within $\sim$10\% for all instruments with respect to \nustar

From Bugs to Decision Support – Leveraging Historical Issue Reports in Software Evolution

Software developers in large projects work in complex information landscapes and staying on top of all relevant software artifacts is an acknowledged challenge. As software systems often evolve over many years, a large number of issue reports is typically managed during the lifetime of a system, representing the units of work needed for its improvement, e.g., defects to fix, requested features, or missing documentation. Efficient management of incoming issue reports requires the successful navigation of the information landscape of a project. In this thesis, we address two tasks involved in issue management: Issue Assignment (IA) and Change Impact Analysis (CIA). IA is the early task of allocating an issue report to a development team, and CIA is the subsequent activity of identifying how source code changes affect the existing software artifacts. While IA is fundamental in all large software projects, CIA is particularly important to safety-critical development. Our solution approach, grounded on surveys of industry practice as well as scientific literature, is to support navigation by combining information retrieval and machine learning into Recommendation Systems for Software Engineering (RSSE). While the sheer number of incoming issue reports might challenge the overview of a human developer, our techniques instead benefit from the availability of ever-growing training data. We leverage the volume of issue reports to develop accurate decision support for software evolution. We evaluate our proposals both by deploying an RSSE in two development teams, and by simulation scenarios, i.e., we assess the correctness of the RSSEs' output when replaying the historical inflow of issue reports. In total, more than 60,000 historical issue reports are involved in our studies, originating from the evolution of five proprietary systems for two companies. Our results show that RSSEs for both IA and CIA can help developers navigate large software projects, in terms of locating development teams and software artifacts. Finally, we discuss how to support the transfer of our results to industry, focusing on addressing the context dependency of our tool support by systematically tuning parameters to a specific operational setting

Performance Evaluation of Serverless Applications and Infrastructures

Context. Cloud computing has become the de facto standard for deploying modern web-based software systems, which makes its performance crucial to the efficient functioning of many applications. However, the unabated growth of established cloud services, such as Infrastructure-as-a-Service (IaaS), and the emergence of new serverless services, such as Function-as-a-Service (FaaS), has led to an unprecedented diversity of cloud services with different performance characteristics. Measuring these characteristics is difficult in dynamic cloud environments due to performance variability in large-scale distributed systems with limited observability.Objective. This thesis aims to enable reproducible performance evaluation of serverless applications and their underlying cloud infrastructure.Method. A combination of literature review and empirical research established a consolidated view on serverless applications and their performance. New solutions were developed through engineering research and used to conduct performance benchmarking field experiments in cloud environments.Findings. The review of 112 FaaS performance studies from academic and industrial sources found a strong focus on a single cloud platform using artificial micro-benchmarks and discovered that most studies do not follow reproducibility principles on cloud experimentation. Characterizing 89 serverless applications revealed that they are most commonly used for short-running tasks with low data volume and bursty workloads. A novel trace-based serverless application benchmark shows that external service calls often dominate the median end-to-end latency and cause long tail latency. The latency breakdown analysis further identifies performance challenges of serverless applications, such as long delays through asynchronous function triggers, substantial runtime initialization for coldstarts, increased performance variability under bursty workloads, and heavily provider-dependent performance characteristics. The evaluation of different cloud benchmarking methodologies has shown that only selected micro-benchmarks are suitable for estimating application performance, performance variability depends on the resource type, and batch testing on the same instance with repetitions should be used for reliable performance testing.Conclusions. The insights of this thesis can guide practitioners in building performance-optimized serverless applications and researchers in reproducibly evaluating cloud performance using suitable execution methodologies and different benchmark types