Path-Sensitive Oracle Data Selection via Static Analysis

Abstract

A test oracle is a procedure that is used during testing to determine whether software behaves correctly or not. One of most important tasks for a test oracle is to choose oracle data (the set of variables monitored during testing) to observe. However, most literature on test oracles has focused either on formal specification generation or on automated test oracle construction, whereas little work exists for supporting oracle data selection. In this paper, we present a path-sensitive approach, PSODS (path-sensitive oracle data selection), to automatically select oracle data for use by expected value oracles. PSODS ranks paths according to the possibility that potential faults may exist in them, and the ranked paths help testers determine which oracle data should be considered first. To select oracle data for each path, we introduce quantity and quality analysis of oracle data, which use static analysis to estimate oracle data for their substitution capability and fault-detection capability. Quantity analysis can reduce the number of oracle data. Quality analysis can rank oracle data based on their fault-detection capability. By using quantity and quality analysis, PSODS reduces the cost of oracle construction and improves fault-detection efficiency and effectiveness. We have implemented our approach and applied it to a real-world project. The experimental results show that PSODS is efficient in helping testers construct test oracles. Moreover, the oracle datasets produced by our approach are more effective and efficient than output-only oracles at detecting faults.

A Vulnerability Detection Approach for Automated Smart Contract Using Enhanced Machine Learning Techniques

“…The trend of smart contract vulnerability detection techniques [2] has emerged under classifications such as static analysis [3], dynamic analysis [4], and code translation mechanisms, according to research. Static analysis approaches used by vulnerability detection tools [13] include control flow analysis [5], [6], [7], pattern matching [8], [9], data flow analysis [10], [11], and symbolic execution [12]. Since it is convenient and does not involve running the program, static analysis has long been used to scrutinise the complex behaviour of programs.…”