Learning Concise Models from Long Execution Traces

Abstract models of system-level behaviour have applications in design exploration, analysis, testing and verification. We describe a new algorithm for automatically extracting useful models, as automata, from execution traces of a HW/SW system driven by software exercising a use-case of interest. Our algorithm leverages modern program synthesis techniques to generate predicates on automaton edges, succinctly describing system behaviour. It employs trace segmentation to tackle complexity for long traces. We learn concise models capturing transaction-level, system-wide behaviour--experimentally demonstrating the approach using traces from a variety of sources, including the x86 QEMU virtual platform and the Real-Time Linux kernel

Inferring behavioral specifications from large-scale repositories by leveraging collective intelligence

Despite their proven benefits, useful, comprehensible, and efficiently checkable specifications are not widely available. This is primarily because writing useful, non-trivial specifications from scratch is too hard, time consuming, and requires expertise that is not broadly available. Furthermore, the lack of specifications for widely-used libraries and frameworks, caused by the high cost of writing specifications, tends to have a snowball effect. Core libraries lack specifications, which makes specifying applications that use them expensive. To contain the skyrocketing development and maintenance costs of high assurance systems, this self-perpetuating cycle must be broken. The labor cost of specifying programs can be significantly decreased via advances in specification inference and synthesis, and this has been attempted several times, but with limited success. We believe that practical specification inference and synthesis is an idea whose time has come. Fundamental breakthroughs in this area can be achieved by leveraging the collective intelligence available in software artifacts from millions of open source projects. Finegrained access to such data sets has been unprecedented, but is now easily available. We identify research directions and report our preliminary results on advances in specification inference that can be had by using such data sets to infer specifications

Inferring Latent States and Refining Force Estimates via Hierarchical Dirichlet Process Modeling in Single Particle Tracking Experiments

Optical microscopy provides rich spatio-temporal information characterizing in vivo molecular motion. However, effective forces and other parameters used to summarize molecular motion change over time in live cells due to latent state changes, e.g., changes induced by dynamic micro-environments, photobleaching, and other heterogeneity inherent in biological processes. This study focuses on techniques for analyzing Single Particle Tracking (SPT) data experiencing abrupt state changes. We demonstrate the approach on GFP tagged chromatids experiencing metaphase in yeast cells and probe the effective forces resulting from dynamic interactions that reflect the sum of a number of physical phenomena. State changes are induced by factors such as microtubule dynamics exerting force through the centromere, thermal polymer fluctuations, etc. Simulations are used to demonstrate the relevance of the approach in more general SPT data analyses. Refined force estimates are obtained by adopting and modifying a nonparametric Bayesian modeling technique, the Hierarchical Dirichlet Process Switching Linear Dynamical System (HDP-SLDS), for SPT applications. The HDP-SLDS method shows promise in systematically identifying dynamical regime changes induced by unobserved state changes when the number of underlying states is unknown in advance (a common problem in SPT applications). We expand on the relevance of the HDP-SLDS approach, review the relevant background of Hierarchical Dirichlet Processes, show how to map discrete time HDP-SLDS models to classic SPT models, and discuss limitations of the approach. In addition, we demonstrate new computational techniques for tuning hyperparameters and for checking the statistical consistency of model assumptions directly against individual experimental trajectories; the techniques circumvent the need for "ground-truth" and subjective information.Comment: 25 pages, 6 figures. Differs only typographically from PLoS One publication available freely as an open-access article at http://journals.plos.org/plosone/article?id=10.1371/journal.pone.013763