SMU Assistant Professor of Information Systems David Lo, together with colleagues from SMU, has developed an automated debugging approach called Adaptive Multimodal Bug Localisation (AML). AML gleans debugging hints from both bug reports and test cases, and then performs a statistical analysis to pinpoint programme elements that are likely to contain bugs.  “While most past studies only demonstrate the applicability of similar solutions for small programmes and ‘artificial bugs’ [bugs that are intentionally inserted into a programme for testing purposes], our approach can automate the debugging process for many real bugs that impact large programmes,” Assistant Prof Lo noted. AML has been successfully evaluated on programmes with more than 300,000 lines of code. By automatically identifying buggy code, developers can save time and redirect their debugging effort to designing new software features for clients. Assistant Prof Lo and his colleagues are now planning to contact several industry partners to take AML one step closer toward integration as a software development tool.