Alice X. Zheng

Scalable statistical bug isolation

We present a statistical debugging algorithm that isolates bugs in programs containing multiple undiagnosed bugs. Earlier statistical algorithms that focus solely on identifying predictors that correlate with program failure perform poorly when there are multiple bugs. Our new technique separates the effects of different bugs and identifies predictors that are associated with individual bugs. These predictors reveal both the circumstances under which bugs occur as well as the frequencies of failure modes, making it easier to prioritize debugging efforts. Our algorithm is validated using several case studies, including examples in which the algorithm identified previously unknown, significant crashing bugs in widely used systems.

Bug isolation via remote program sampling

We propose a low-overhead sampling infrastructure for gathering information from the executions experienced by a programs user community. Several example applications illustrate ways to use sampled instrumentation to isolate bugs. Assertion-dense code can be transformed to share the cost of assertions among many users. Lacking assertions, broad guesses can be made about predicates that predict program errors and a process of elimination used to whittle these down to the true bug. Finally, even for non-deterministic bugs such as memory corruption, statistical modeling based on logistic regression allows us to identify program behaviors that are strongly correlated with failure and are therefore likely places to look for the error.

Failure diagnosis using decision trees

We present a decision tree learning approach to diagnosing failures in large Internet sites. We record runtime properties of each request and apply automated machine learning and data mining techniques to identify the causes of failures. We train decision trees on the request traces from time periods in which user-visible failures are present. Paths through the tree are ranked according to their degree of correlation with failure, and nodes are merged according to the observed partial order of system components. We evaluate this approach using actual failures from eBay, and find that, among hundreds of potential causes, the algorithm successfully identifies 13 out of 14 true causes of failure, along with 2 false positives. We discuss some results in applying simplified decision trees on eBays production site for several months. In addition, we give a cost-benefit analysis of manual vs. automated diagnosis systems. Our contributions include the statistical learning approach, the adaptation of decision trees to the context of failure diagnosis, and the deployment and evaluation of our tools on a high-volume production service.

Statistical debugging: simultaneous identification of multiple bugs

We describe a statistical approach to software debugging in the presence of multiple bugs. Due to sparse sampling issues and complex interaction between program predicates, many generic off-the-shelf algorithms fail to select useful bug predictors. Taking inspiration from bi-clustering algorithms, we propose an iterative collective voting scheme for the program runs and predicates. We demonstrate successful debugging results on several real world programs and a large debugging benchmark suite.

Statistical Network Analysis: Models, Issues, and New Directions

Invited Presentations.- Structural Inference of Hierarchies in Networks.- Heider vs Simmel: Emergent Features in Dynamic Structures.- Joint Group and Topic Discovery from Relations and Text.- Statistical Models for Networks: A Brief Review of Some Recent Research.- Other Presentations.- Combining Stochastic Block Models and Mixed Membership for Statistical Network Analysis.- Exploratory Study of a New Model for Evolving Networks.- A Latent Space Model for Rank Data.- A Simple Model for Complex Networks with Arbitrary Degree Distribution and Clustering.- Discrete Temporal Models of Social Networks.- Approximate Kalman Filters for Embedding Author-Word Co-occurrence Data over Time.- Discovering Functional Communities in Dynamical Networks.- Empirical Analysis of a Dynamic Social Network Built from PGP Keyrings.- Extended Abstracts.- A Brief Survey of Machine Learning Methods for Classification in Networked Data and an Application to Suspicion Scoring.- Age and Geographic Inferences of the LiveJournal Social Network.- Inferring Organizational Titles in Online Communication.- Learning Approximate MRFs from Large Transactional Data.- Panel Discussion.- Panel Discussion.