Matching Test Cases for Effective Fault Localization

Finding the cause of a program’s failure from a causal-analysis perspective requires, for each statement, tests that cover the statement and tests that do not cover the statement. However, in practice the composition of test suites can be detrimental to effective fault localization for two reasons: (1) lack-of-balance, which occurs if the coverage characteristics of tests that cover a statement differ from tests that do not cover the statement, and (2) lack-of-overlap, which occurs if test cases that reach the control-dependence predecessor of a statement cover or do not cover the statement. This paper addresses these two problems. First, the paper presents empirical results that show that, for effective fault localization, the composition of test suites should exhibit balance and overlap. Second, the paper presents new techniques to overcome these problems—matching to address lack-of-balance and causal-effect imputation to overcome lack-of-overlap—and presents empirical evidence that these techniques increase the effectiveness of fault localization

On-line anomaly detection of deployed software: a statistical machine learning approach

This paper presents a new machine-learning technique that performs anomaly detection as software is executing in the field. The technique uses a fully observable Markov model where each state in the model emits a number of distinct observations according to a probability distribution, and estimates the model parameters using the Baum-Welch algorithm. The trained model is then deployed with the software to perform anomaly detection. By performing the anomaly detection as the software is executing, faults associated with anomalies can be located and fixed before they cause critical failures in the system, and developers time to debug deployed software can be reduced. This paper also presents a prototype implementation of our technique, along with a case study that shows, for the subjects we studied, the effectiveness of the technique