New Labeled Dataset of Interconnected Lexical Typos for Automatic Correction in the Bug Reports

Abstract

Large-scale and especially open-source projects use software triage systems like Bugzilla to manage their user’s requests like bugs, suggestions, requirements. The software triage systems have many tasks like prioritizing, finding duplicate and assigning bug reports to developers automatically, which needs text mining, information retrieval, and natural language processing techniques. We already showed there are many typos in the bug reports which reduce the performance of artificial intelligence techniques. The connected terms were one of the most types of typos in the context of bug reports. Also, we introduce some algorithms to correct the connected terms earlier, but there was not any labeled dataset that can be used to evaluate the accuracy of typo correction. Now we made a new labeled dataset including 42,970 typos between 182,096 to can be used for the typo cor-rection evaluation process. There are 52% connected typos in the labeled dataset, which show the previous results about the number of connected typos were cor-rect. Then we used the typo correction algorithms which were introduced in prior studies to evaluate their accuracy. The experimental results show 81.6 % and 83.3 percent accuracy in top-5 and top-10 suggestions of the list of typo corrections, respectively.