Predicting the bug fixing time using word embedding and deep long short term memories

Abstract

In bug fixing process, estimating the 'Time to Fix Bug' is one of the factors that helps the triager to allocate jobs in a better way. Due to the limitation of resources for bug fixing, the bugs with long fixing time must be identified, as soon as possible, after receiving the report. This helps the prioritisation and fixing process of the bug reports. In the process of bug fixing, a temporal sequence of activities is done. Each activity is represented by a term. Useful semantic information and longterm dependency are available between terms in the sequence, but it is usually underutilised by existing bug fixing time predictor approaches. This work presents a novel deep learning-based model (called DeepLSTMPred) that (i) converts constituent terms to a vector of real numbers by considering their semantic meaning, (ii) finds the long-term dependencies between terms by deep long short term memory (LSTM) and (iii) classifies sequences to short fixing time or long fixing time. DeepLSTMPred is evaluated on bug reports extracted from the Mozilla project. The results show that the proposed method has better performance in comparison with a state-of-the-art approach (that is the hidden Markov-based model). The experimental results show that DeepLSTMPred achieves 15-20% improvement in terms of accuracy, precision, f-score, and recall.

On the prediction of long-lived bugs: An analysis and comparative study using FLOSS projects

“…In addition, two different approaches were used to separate software bugs with short and long life cycles. The first approach consisted of transforming the bug-fixing times into a discrete value based on quartiles using 25%(Q1), 50% (Q2 or median), or 75% (Q3) [33,6,8,9,10]. However, these authors used different names to refer to each quartile (e.g., slowly-fixed bugs or not very fast refers to Q1).…”

“…To yield more stable models in our experiments, we trained and tested each model using the repeated 10 × 5 fold crossvalidation technique [41]. 10 Finally, we performed the Wilcoxon signed-rank statistical test [42] (with a significance level of 95%) to evaluate the statistical significance among the ML algorithm performances.…”

“…Estimating the "bug fix time" is essential for many stakeholders [6,7]. For software managers, it is one of the main factors that helps them to perform such allocation more effectively [8,9,10]. In large system projects with tight schedules and limited resources, it may not be possible to close all known bugs before the upcoming release.…”

A Versatile Dataset of Agile Open Source Software Projects

“…This period can be considered as an approximation of the time taken by the development team to resolve the issue. This is usually the target variable used for bug resolution/fixing time estimation [14,18,27]. Other proxies for time are provided, such as In_Progress_Time and Total_Effort_Time, indicating, respectively, the implementation time and the development (including code review and testing) time.…”

“…Additionally, the dataset provides other useful information that can be considered for optimising task assignment, for example, considering developers' work load [5]. The dataset also provides the issue status transitions, which can be used to analyse activities and events to predict the time to fix a bug, or bug triage [14,18,27].…”