Redundancy-based automated program repair (APR), which generates patches by referencing existing source code, has gained much attention since they are effective in repairing real-world bugs with good interpretability. However, since existing approaches either demand the existence of multiline similar code or randomly reference existing code, they can only repair a small number of bugs with many incorrect patches, hindering their wide application in practice. In this work, we aim to improve the effectiveness of redundancy-based APRs by exploring more effective source code reuse methods for improving the number of correct patches and reducing incorrect patches. Specifically, we have proposed a new repair technique named REPATT, which incorporates a two-level pattern mining process for guiding effective patch generation (i.e., token and expression levels). We have conducted an extensive experiment on the widely-used Defects4J benchmark and compared Repatt with ten state-of-the-art APR approaches. The results show that it complements existing approaches by repairing 9 unique bugs compared with the latest Large Language Model (LLM)-based and deep learning-based methods and 19 unique bugs compared with traditional repair methods when providing the perfect fault localization. In addition, when the perfect fault localization is unknown in real practice, REPATT significantly outperforms the baseline approaches by achieving much higher patch precision, i.e., 83.8 %, although it repairs fewer bugs. Moreover, we further proposed an effective patch ranking strategy for combining the strength of REPATT and the baseline methods. The result shows that it repairs 124 bugs when only considering the Top-1 patches and improves the best-performing repair method by repairing 39 more bugs. The results demonstrate the effectiveness of our approach for practical use.

Fault localization, aiming at localizing the root cause of the bug under repair, has been a longstanding research topic. Although many approaches have been proposed in past decades, most of the existing studies work at coarse-grained statement or method levels with very limited insights about how to repair the bug ( granularity problem ), but few studies target the finer-grained fault localization. In this article, we target the granularity problem and propose a novel finer-grained variable-level fault localization technique. Specifically, the basic idea of our approach is that fault-relevant variables may exhibit different values in failed and passed test runs, and variables that have higher discrimination ability have a larger possibility to be the root causes of the failure. Based on this, we propose a program-dependency-enhanced decision tree model to boost the identification of fault-relevant variables via discriminating failed and passed test cases based on the variable values. To evaluate the effectiveness of our approach, we have implemented it in a tool called VarDT and conducted an extensive study over the Defects4J benchmark. The results show that VarDT outperforms the state-of-the-art fault localization approaches with at least 268.4% improvement in terms of bugs located at Top-1, and the average improvement is 351.3%. Besides, to investigate whether our finer-grained fault localization result can further improve the effectiveness of downstream APR techniques, we have adapted VarDT to the application of patch filtering, where we use the variables located by VarDT to filter incorrect patches. The results denote that VarDT outperforms the state-of-the-art PATCH-SIM and BATS by filtering 14.8% and 181.8% more incorrect patches, respectively, demonstrating the effectiveness of our approach. It also provides a new way of thinking for improving automatic program repair techniques.

Highly accurate speed prediction technology is of great significance for online implementation of energy management strategy (EMS). Due to the complex and variable driving conditions, the accuracy of conventional speed prediction method is yet to be improved by driving pattern adaption. This paper proposes a vehicle speed prediction method based on long- and short-term memory neural network (LSTM) with driving pattern recognition and integrates it in energy management framework based on model predictive control (MPC). First, similar samples belonging to the same driving pattern are selected offline to train a more efficient and targeted LSTM. Then an online speed prediction algorithm based on driving pattern recognition is proposed. The results show that root mean square error (RMSE) of the whole driving cycle is reduced by 39% compared to conventional LSTM. Meanwhile, fuel economy and fuel cell system (FCS) durability are improved, which proves the effectiveness of the proposed method. And the real-time applicability of the proposed predictive EMS is verified.