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, and 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 process of typo correction. Now we made a new labeled dataset including 42,970 typos between 182,096 to can be used for the typo correction evaluation process. There are 52% connected typos in the labeled dataset, which show the previous results about the number of connected typos were correct. 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% accuracy in top-5 and top-10 suggestions of the list of typo corrections, respectively.

Duplicate Detection Models for Bug Reports of Software Triage Systems: A Survey

“…The first methodology called the information retrieval-based approach, which its procedure is shown in Figure 1. In the first box, the raw dataset of bug reports exists which should be preprocessed in box 2 till deal with null values, unify the data type of some fields like version and priority and preferably change them to numerical, remove stop words from textual fields, stemming textual fields, correcting the typos in textual DFs [5,8] [9], and so on [1,4]. The feature extraction phase of box 6 returns a numerical vector consist of many similarity metrics as box 7.…”