Utilizing Risk Number and Program Slicing to Improve Human-Machine Pair Inspection

Human-Machine Pair Inspection (HMPI) is a novel code inspection technology proposed in our previous work, which is the style that machine will intelligently guide the programmer to carry out inspections of the program code during programming. For large-scale software projects, the efficiency of HMPI needs to be improved due to the inaccurate measurement of the code structure and the excessive inspection scope. In this paper, to alleviate the above deficiencies, we propose the Risk Number, a code evaluation metric generated based on historical error data. The Risk Number is calculated by a statistical tool called regression analysis, which more accurately indicates the relationship between the nested structure of the code and the likelihood of containing bugs than Cognitive Complexity. Additionally, HMPI is supported by utilizing Risk Number to point out high-risk code and program slicing techniques to extract statements that have dependencies on the code to generate checklists, thereby reducing the scope of inspection. We describe a case study to evaluate the performance of this method by comparing its inspection time and number of detected errors with our previous work. The result shows that the method is likely to guide the programmer to inspect the faulty code earlier and be more efficient in detecting defects than HMPI based on Cognitive Complexity.