W. Eric Wong

Peggy Cellier, Mireille Ducassé, Sébastien Ferré et al.

This chapter illustrates the basic concepts of fault localization using a data mining technique. It utilizes the Trityp program to illustrate the general method. Formal concept analysis and association rule are two well-known methods for symbolic data mining. In their original inception, they both consider data in the form of an object-attribute table. In their original inception, they both consider data in the form of an object-attribute table. The chapter considers a debugging process in which a program is tested against different test cases. Two attributes, PASS and FAIL, represent the issue of the test case. The chapter extends the analysis of data mining for fault localization for the multiple fault situations. It addresses how data mining can be further applied to fault localization for GUI components. Unlike traditional software, GUI test cases are usually event sequences, and each individual event has a unique corresponding event handler.

Shiyi Kong, Jun Ai, Minyan Lu et al.

Modern software systems have become increasingly complex, which makes them difficult to test and validate. Detecting software partial anomalies in complex systems at runtime can assist with handling unintended software behaviors, avoiding catastrophic software failures and improving software runtime availability. These detection techniques aim to identify the manifestation of faults (anomalies) before they ultimately lead to unavoidable failures, thus, supporting the following runtime fault-tolerant techniques. In this work, we propose a novel anomaly detection method named DistAD, which is based on the distribution of software runtime dynamic execution traces. Unlike other existing works using key performance indicators, the execution trace is collected during runtime via intrusive instrumentation. Instrumentation are controlled following a sampling mechanism to avoid excessive overheads. Bi-directional Long Short-Term Memory (Bi-LSTM), an architecture of Recurrent Neural Network (RNN) is used to achieve the anomaly detection. The whole framework is constructed under a One-Class Neural Network (OCNN) learning mode which can help eliminate the limits of lacking for enough labeled samples and the data imbalance issues. A series of controlled experiments are conducted on a widely used database system named Cassandra to prove the validity and feasibility of the proposed method. Overheads brought about by the intrusive probing are also evaluated. The results show that DistAD can achieve more than 70% accuracy and 90% recall (in normal states) with no more than 2 times overheads compared with unmonitored executions.