Mistakes in boundary conditions are the cause of many bugs in software. These mistakes happen when, e.g., developers make use of '<' or '>' in cases where they should have used '<=' or '>='. Mistakes in boundary conditions are often hard to find and manually detecting them might be very time-consuming for developers. While researchers have been proposing techniques to cope with mistakes in the boundaries for a long time, the automated detection of such bugs still remains a challenge. We conjecture that, for a tool to be able to precisely identify mistakes in boundary conditions, it should be able to capture the overall context of the source code under analysis. In this work, we propose a deep learning model that learn mistakes in boundary conditions and, later, is able to identify them in unseen code snippets. We train and test a model on over 1.5 million code snippets, with and without mistakes in different boundary conditions. Our model shows an accuracy from 55% up to 87%. The model is also able to detect 24 out of 41 real-world bugs; however, with a high false positive rate. The existing state-of-the-practice linter tools are not able to detect any of the bugs. We hope this paper can pave the road towards deep learning models that will be able to support developers in detecting mistakes in boundary conditions.@en

Offline reinforcement learning (RL), or learning from a fixed data set, is an attractive alternative to online RL. Offline RL promises to address the cost and safety implications of tak- ing numerous random or bad actions online, a crucial aspect of traditional RL that makes it difficult to apply in real-world problems. However, when RL is na ̈ıvely applied to a fixed data set, the resulting policy may exhibit poor performance in the real environment. This happens due to over-estimation of the value of state-action pairs not sufficiently covered by the data set. A promising way to avoid this is by applying pessimism and acting according to a lower bound estimate on the value. It has been shown that penalizing the learned value according to a pessimistic bound on the uncertainty can drastically improve offline RL. In deep reinforcement learn- ing, however, uncertainty estimation is highly non-trivial and development of effective uncertainty-based pessimistic algo- rithms remains an open question. This paper introduces two novel offline deep RL methods built on Double Deep Q- Learning and Soft Actor-Critic. We show how a multi-headed bootstrap approach to uncertainty estimation is used to cal- culate an effective pessimistic value penalty. Our approach is applied to benchmark offline deep RL domains, where we demonstrate that our methods can often beat the current state- of-the-art.@en