Leveraging text analytics to improve software testing

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Software testing is the common practice to ensure software correctness and discover as many bugs before software release. A fundamental challenge of software testing is that the number of execution paths grow exponentially as the program size increases and can be infinite. For most real-world programs, it is impossible to test all execution paths. In practice, one needs to use search heuristics to prioritize execution paths, compromise accuracy by combining paths, and use other techniques to test programs. Valid program inputs typically need to follow certain constraints. Focusing on valid or close-to-valid inputs (for boundary cases) can help test the core functionalities of the program, which improves testing coverage and effectiveness. Traditional software testing techniques are unaware of input constraints, and thus unaware of which paths lead to the processing of valid inputs. This project will automatically extract input constraints from documents by using text analytics techniques. It will then use these input constraints to favour execution paths that execute a program's core functionalities during software testing. Testing with invalid inputs is also very important, e.g., to check error-handling code or find defects due to malformed inputs. Therefore, strategies to negate the constraints efficiently for testing error-handling code will also be explored. Regardless of what inputs (valid or invalid) to focus on, one cannot do so without knowing what inputs are valid and what are not. The proposed approach enables this choice by extracting input constraints from software documents automatically. In addition, new approaches will be designed to analyze other software text to guide software testing to focus on code regions that are more likely to contain defects. These approaches will enhance the effectiveness of automatic test generation and bug detection.