Temporal Modelling of Bug Numbers of Open Source Software Applications Using LSTM

Predicting the number of bugs in any software application is an important but challenging task. The software manager by modelling the bug numbers, can take timely decisions in reducing the amount of effort investment and also the allocation of resources. The software developers can also take effective steps for reducing the number of bugs in the future version of the software application. The end users also can make a timely decision on adoption of a particular software application by knowing the growth pattern of bugs in advance. The challenges behind modeling the bug growth patterns are random causes behind a bug. A bug in any software may be caused during testing, development or application. Causal modelling of bug numbers is a complex and tedious task as they consider many internal characteristics to be modelled. In this paper, we have used we have used Long Short Term Memory (LSTM) [14] Network for temporal modelling the bug numbers of three different software applications. We have used both univariate and multivariate modelling approach to predict bug number in advance. The goal is to have an appropriate model for software bug growth pattern.