Angelix: Scalable Multiline Program Patch Synthesis via Symbolic Analysis

By Sergey Mechtaev, Jooyong Yi, Abhik Roychoudhury. International Conference on Software Engineering, 2016

638 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Software, Signal Processing

View Paper: https://www.semanticscholar.org/paper/3f215e83b39a0887257a03274002f353c5a57537

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Software

In Signal Processing

Citations in the corpus

CoCoNuT: combining context-aware neural translation models using ensemble for program repair (2020, methodology): “Stdard 1 Angelix [59] 318/591 18/39 2 Prophet [55] 301/839 15/39 3 SPR [52] 283/783 11/38 4 Astor* [58] 6/11 5 LSRepair [45] 19/37 6 DLFix [42] 29/65 -”
An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation (2018, methodology): “e avoid over-fitting patches to test cases. A complementary set of repair techniques leverage program analysis and program synthesis to repair programs by constructing code with particular properties [36,41,52,53,61,64,78,93]. Some approaches perform automated program repair by searching the fix amo”
SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair (2018, neutral): “, Angelix [2]) and dynamic analysis (e.”
Precise Condition Synthesis for Program Repair (2016, methodology): “Some of the other state-of-the-art systems, such as Angelix [19] and Prophet [16], were designed for C and cannot be directly compared.”
CODIT: Code Editing With Tree-Based Neural Models (2018, methodology): “Synthesis based program repair tools synthesizes program elements through symbolic execution of test cases [89], [90].”
Accelerating search-based program synthesis using learned probabilistic models (2018, background): “As a result, recent years have witnessed a surge of interest in applying this technology to a wide range of problems, including end-user programming [11], intelligent tutoring [25], circuit transformation [8], and program repair [18], among many others.”
Compilation Error Repair: For the Student Programs, From the Student Programs (2018, methodology): “In that study, repairs were semantic repairs and were generated by four different tools, namely GenProg [12], AE [26], Angelix [14], and Prophet [13].”
AutoCodeRover: Autonomous Program Improvement (2024, neutral): “Semantics-based APR techniques [22, 24] generate patches by formulating a repair constraint that needs to be satisfied based on a given test suite specification, and then solving the repair constraint to generate patches.”
Automated Program Repair in the Era of Large Pre-trained Language Models (2022, neutral): “Traditional APR tools can be classiﬁed as heuristic-based [7]–[9], constraint-based [10]–[12] and template-based [13]–[16], [19].”
Agentless: Demystifying LLM-based Software Engineering Agents (2024, background): “Traditional APR techniques can be categorized as template-based [57, 39], heuristic-based [51, 50], and constraint-based [65, 61]”
Context-Aware Patch Generation for Better Automated Program Repair (2018, methodology): “Actually, these kind of bugs can hardly been repaired successfully by ACS, as well as semantics-based APR [23, 32, 33, 53], since they mainly focus on synthesizing conditions or right-hand side of assignments.”
Automated program repair (2019, methodology): “Symbolic analysis-based approaches such as Mechtaev et al.16 on the other hand, avoid exhaustive enumeration of possible angelic values.”
Repairing Bugs in Python Assignments Using Large Language Models (2022, background): “Existing approaches have applied a variety of technical ideas, including formal program logic [5], [31], [32], search-based techniques [33] like genetic programming [2], [34], [35], and machine learning [3], [4], [36].”