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Software analytics is to utilize data-driven approaches to enable software practitioners to perform data exploration and analysis in order to obtain insightful and actionable information for completing various tasks around software systems, software users, and software development process. In this article, we present how we worked on the StackMine project, a successful software analytic project that produced an analytic system used by and transferred to Microsoft product teams. We also discuss the lessons learned from StackMine on applying software analytics technologies to make practice impact -solving problems that practitioners care about, using domain knowledge for correct data understanding, building prototypes early to get practitioners' feedback, taking scalability and customizability into account, and evaluating analysis results using criteria related to real tasks.

Software hangs can be caused by expensive operations in responsive actions (such as time-consuming operations in UI threads). Some of the expensive operations depend on the input workloads, referred to as workload-dependent performance bottlenecks (WDPBs). WDPBs are usually caused by workload-dependent loops (i.e., WDPB loops) that contain relatively expensive operations. Traditional performance testing and single-execution profiling may not reveal WDPBs due to incorrect assumptions of workloads. To address these issues, we propose the ∆Infer approach that predicts WDPB loops under large workloads via inferring iteration counts of WDPB loops using complexity models for the workload size. ∆Infer incorporates a novel concept named context-sensitive delta inference that consists of two parts: temporal inference for inferring the complexity models of different program locations, and spatial inference for identifying WDPB loops as WDPB candidates. We conducted evaluations on two popular open-source GUI applications, and identified impactful WDPBs that caused 10 performance bugs.

The (variational) graph auto-encoder and its variants have been popularly used for representation learning on graph-structured data. While the encoder is often a powerful graph convolutional network, the decoder reconstructs the graph structure by only considering two nodes at a time, thus ignoring possible interactions among edges. On the other hand, structured prediction, which considers the whole graph simultaneously, is computationally expensive. In this paper, we utilize the well-known triadic closure property which is exhibited in many real-world networks. We propose the triad decoder, which considers and predicts the three edges involved in a local triad together. The triad decoder can be readily used in any graph-based auto-encoder. In particular, we incorporate this to the (variational) graph auto-encoder. Experiments on link prediction, node clustering and graph generation show that the use of triads leads to more accurate prediction, clustering and better preservation of the graph characteristics.

Despite the success and prospects of the robotic catheter system for the cardiovascular access, loss of vision, and haptics have limited its global adoption. A direct implication is the great difficulty posed when trying to eliminate the backlash in catheters during vascular cannulations. As a result, physicians and patients end up been exposed to high radiation for a long period of time. Existing control systems proposed for such interventional robots have not fully consider the hysteretic (backlash) behavior. In this study, a novel robotic catheter system is designed for accessing the human cardiac area through the radial vasculature, while single factor descriptive analysis is employed to characterize the backlash behavior during axial motions of the interventional robot. Based on the descriptive analysis, an adaptive system is proposed for the backlash compensation during the cardiovascular access. The adaptive system consists of a neuro-fuzzy module that predicts a backlash gap based on bounded motion signals, and contact force modulated from a modified error-based force control model. The proposed system is implemented in MATLAB and visual C++. Finally, an in vitro experiment with a human tubular model, shows that the proposed adaptive compensation system can minimize the backlash occurrence during cardiovascular access.

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