Towards Lifelong Learning for Software Analytics Models: Empirical Study on Brown Build and Risk Prediction

Nowadays, software analytics tools using machine learning (ML) models to, for example, predict the risk of a code change are well established. However, as the goals of a project shift over time, and developers and their habits change, the performance of said models tends to degrade (drift) over time, until a model is retrained using new data. Current retraining practices typically are an afterthought (and hence costly), requiring a new model to be retrained from scratch on a large, updated data set at random points in time; also, there is no continuity between the old and new model. In this paper, we propose to use lifelong learning (LL) to continuously build and maintain ML-based software analytics tools using an incremental learner that progressively updates the old model using new data. To avoid so-called ''catastrophic forgetting'' of important older data points, we adopt a replay buffer of older data, which still allows us to drastically reduce the size of the overall training data set, and hence model training time. We empirically evaluate our LL approach on two industrial use cases, i.e., a brown build detector and a Just-in-Time risk prediction tool, showing how LL in practice manages to at least match traditional retraining-from-scratch performance in terms of F1-score, while using 3.3-13.7x less data at each update, thus considerably speeding up the model updating process. Considering both the computational effort of updates and the time between model updates, the LL setup needs 2-40x less computational effort than retraining-from-scratch setups.