ing with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2022 Association for Computing Machinery. 1049-331X/2023/0-ART1 $15.00 https://doi.org/10.1145/nnnnnnn.nnnnnnn ACM Trans. Softw. Eng. Methodol., Vol. 0, No. 0, Article 1. Publication date: 2023. ar X iv :2 30 1. 03 27 0v 1 [ cs .S E ] 9 J an 2 02 3 1:2 Quanjun Zhang, Chunrong Fang, Yuxiang Ma, Weisong Sun, and Zhenyu Chen

A Survey of Learning-based Automated Program Repair

Automated program repair techniques aim to aid software developers with the challenging task of fixing bugs. In heuristic-based program repair, a search space of program variants is created by applying mutation operations on the source code to find potential patches for bugs. Most commonly, every selection of a mutation operator during search is performed uniformly at random. The inefficiency of this critical step in the search creates many variants that do not compile or break intended functionality, wasting considerable resources as a result. In this paper, we address this issue and propose a reinforcement learning-based approach to optimise the selection of mutation operators in heuristic-based program repair. Our solution is programming language, granularity-level, and search strategy agnostic and allows for easy augmentation into existing heuristic-based repair tools. We conduct extensive experimentation on four operator selection techniques, two reward types, two credit assignment strategies, two integration methods, and three sets of mutation operators using 22,300 independent repair attempts. We evaluate our approach on 353 real-world bugs from the Defects4J benchmark. Results show that the epsilon-greedy multi-armed bandit algorithm with average credit assignment is best for mutation operator selection. Our approach exhibits a 17.3% improvement upon the baseline, by generating patches for 9 additional bugs for a total of 61 patched bugs in the Defects4J benchmark.

Reinforcement Learning for Mutation Operator Selection in Automated Program Repair

Code editing is essential in evolving software development. Many automated code editing tools have been proposed that leverage both Information Retrieval-based techniques and Machine Learning-based code generation and code editing models. Each technique comes with its own promises and perils, and they are often used together to complement their strengths and compensate for their weaknesses. This paper proposes a hybrid approach to better synthesize code edits by leveraging the power of code search, generation, and modification. Our key observation is that a patch obtained by search and retrieval, even if imperfect, can provide helpful guidance to a code generation model. However, a retrieval-guided patch produced by a code generation model can still be a few tokens off from the intended patch. Such generated patches can be slightly modified to create the intended patches. SARGAM is a novel tool designed to mimic a real developer's code editing behavior. Given an original code version, the developer may search for related patches, generate or write the code, and then modify the generated code to adapt it to the right context. Our evaluation of SARGAM on edit generation shows superior performance with respect to current state-of-the-art techniques. SARGAM also shows great effectiveness on automated program repair tasks.

Automated Code Editing with Search-Generate-Modify

There has been much work done in the area of automated program repair, specifically through using machine learning methods to correct buggy code. Whereas some degree of success has been attained by those efforts, there is still considerable room for growth with regard to the accuracy of results produced by such tools. In that vein, we implement Hephaestus, a novel method to improve the accuracy of automated bug repair through learning to apply edit operations. Hephaestus leverages neural machine translation and attempts to produce the edit operations needed to correct a given buggy code segment to a fixed version. We examine the effects of using various forms of edit operations in the completion of this task. Our study found that all models which learned from edit operations were not as effective at repairing bugs as models which learned from fixed code segments directly. This evidences that learning edit operations does not offer an advantage over the standard approach of translating directly from buggy code to fixed code. We conduct an analysis of this lowered efficiency and explore why the complexity of the edit operations-based models may be suboptimal. Interestingly, even though our Hephaestus model exhibited lower translation accuracy than the baseline, Hephaestus was able to perform successful bug repair. This success, albeit small, leaves the door open for other researchers to innovate unique solutions in the realm of automatic bug repair.

Can We Automatically Fix Bugs by Learning Edit Operations?

Given the increasing popularity and availability of location tracking devices, large quantities of spatiotemporal data are available from many different sources. Quick interactive analysis of such data is important in order to understand the data, identify patterns, and eventually make a marketable product. Since the data do not necessarily follow the relational model and may require flexible processing possibly using advanced machine learning techniques, spatial databases or similar query tools do not make the best means for such analysis. Moreover, the high complexity of geometric operations makes the quick interactive analysis very difficult. In this paper, we present a highly flexible functional query engine that 1) works with multiple schema types, 2) provides fast response times by spatiotemporal indexing and parallelization, 3) helps understand the data using visualizations and 4) is highly extensible to easily add complex functionality. To demonstrate its usefulness, we use our tool to solve a real world problem of crime pattern analysis in Los Angeles County and compare the process with other well known tools.

Aaron Harris

Papers

Can We Automatically Fix Bugs by Learning Edit Operations?

A parallel query engine for interactive spatiotemporal analysis