Discovering Software Process Deviations Using Visualizations

Peer reviewe

Enhancing the sensitivity of magnetic sensors by 3D metamaterial shells

Magnetic sensors are key elements in our interconnected smart society. Their sensitivity becomes essential for many applications in fields such as biomedicine, computer memories, geophysics, or space exploration. Here we present a universal way of increasing the sensitivity of magnetic sensors by surrounding them with a spherical metamaterial shell with specially designed anisotropic magnetic properties. We analytically demonstrate that the magnetic field in the sensing area is enhanced by our metamaterial shell by a known factor that depends on the shell radii ratio. When the applied field is non-uniform, as for dipolar magnetic field sources, field gradient is increased as well. A proof-of-concept experimental realization confirms the theoretical predictions. The metamaterial shell is also shown to concentrate time-dependent magnetic fields upto frequencies of 100 kHz

Detecting dependencies in Enterprise JavaBeans with SQuAVisiT

We present recent extensions to SQuAVisiT, Software Quality Assessment and Visualization Toolset. While SQuAVisiT has been designed with traditional software and traditional caller-callee dependencies in mind, recent popularity of Enterprise JavaBeans (EJB) required extensions that enable analysis of additional forms of dependencies: EJB dependency injections, object-relational (persistence) mappings and Web service mappings. In this paper we discuss the implementation of these extensions in SQuAVisiT and the application of SQuAVisiT to an open-source software system. Keywords: Java, Visualization, Containers, Web services, Open source software, Computer architectur

Measurement of the I-V characteristics of superconducting dipoles : automatic compensation of low frequency drift

Superconducting microbridges and Josephson junctions showing RSJ-like I-V characteristics have potential applications through their current I dependences, at fixed bias voltage V, versus various parameters such as temperature (T), magnetic field (B) or incident optical power (P)... The main problem associated with both low values of voltage (µV-mV range) and dynamic resistance Rd ([MATH] range) was solved earlier ; however, the measurement system still suffers from excessive 1/f noise due to static biasing conditions if neither the incoming signals nor the preamplifier can be chopped. We have overcome this difficulty through the use of a periodic sampling of four points of the I-V characteristic taking advantage of the odd symmetry of this characteristic. In each period, two of the samples occur in the superconducting state and give the zero voltage reference of the measurement system. This allows us to automatically compensate for the low frequency drifts occuring at preamplifier level. The two other periodic samples, opposite in sign, must appear symmetric with respect to the zero voltage reference at the preamplifier output. This constraint is also used to automatically compensate for the low frequency drifts of the ac square signal which controls the I-V operating point. For example, a high Tc superconducting microbridge, used as a temperature sensor, has an equivalent low frequency drift of 0.4 mKpp and 6 mKpp respectively with and without the automatic control

Recognizing gender of stack overflow users

Software development remains a predominantly male activity, despite coordinated efforts from research, industry, and policy makers. This gender imbalance is most visible in social programming, on platforms such as Stack Overflow. To better understand the reasons behind this disparity, and offer support for (corrective) decision making, we and others have been engaged in large-scale empirical studies of activity in these online platforms, in which gender is one of the variables of interest. However, since gender is not explicitly recorded, it is typically inferred by automatic "gender guessers", based on cues derived from an individual's online presence, such as their name and profile picture. As opposed to self-reporting, used in earlier studies, gender guessers scale better, but their accuracy depends on the quantity and quality of data available in one's online profile. In this paper we evaluate the applicability of different gender guessing approaches on several datasets derived from Stack Overflow. Our results suggest that the approaches combining different data sources perform the best