Staged program repair with condition synthesis

By Fan Long, Martin C. Rinard. ESEC/SIGSOFT FSE, 2015

411 citations recorded by the source corpus.

Subfields: Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/6fe2f67fbd8526daaeb87b2c7940aa4b5749c550

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Citations in the corpus

CURE: Code-Aware Neural Machine Translation for Automatic Program Repair (2021, methodology): “Automatic Program Repair: Many APR techniques have been proposed, which use genetic programming [1], condition synthesis [2], [5], [9], state abstraction [14], heuristics [8], [10], [12], [62], human-designed fix patterns [3], [6], mined fix patterns [4], [7], [13], [15], [56], [63], bytecode mutati”
Automated program repair (2019, background): “Techniques that work on enumerating possible angelic values without adopting symbolic execution [21, 22] typically try to repair conditional statements exclusively, where the angelic values are exhaustively enumerated until all failing test cases pass.”
Precise Condition Synthesis for Program Repair (2016, methodology): “SPR [6] uses a dedicated search algorithm to solve the search problem.”
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 -”
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]”
Nopol: Automatic Repair of Conditional Statement Bugs in Java Programs (2018, methodology): “Test-suite based repair, which has been popularized by the work of GenProg by Le Goues et al. [27], has become an actively explored research area [38], [24], [44], [45], [29].”
Context-Aware Patch Generation for Better Automated Program Repair (2018, background): “SPR and Prophet only synthesize program elements involving conditions.”
Automated Program Repair via Conversation: Fixing 162 out of 337 Bugs for $0.42 Each using ChatGPT (2023, positive): “Classic APR techniques can be mainly classified as heuristic-based [35, 37, 67], constraint-based [16, 34, 43, 50] and template-based [23, 26, 40, 41, 49] ones.”
Chapter Six - Mutation Testing Advances: An Analysis and Survey (2019, positive): “For instance, Long and Rinard [256] and Kim et al. [257] use syntactic patterns to perform program repair.”
Less training, more repairing please: revisiting automated program repair via zero-shot learning (2022, background): “Additionally, several traditional APR tools [17, 21, 48, 76] focus solely on fixing conditional statement related bugs, showing the importance of targeting common bug patterns.”
SQLizer: query synthesis from natural language (2017, methodology): “…refinement strategy in Sqlizer is inspired by prior work on fault localization [Ball et al. 2003; Groce and Visser 2003; Jones and Harrold 2005; Jones et al. 2002; Jose and Majumdar 2011a,b] and program repair [Goues et al. 2012; Long and Rinard 2015, 2016; Nguyen et al. 2013; Weimer et al. 2009].”
Angelix: Scalable Multiline Program Patch Synthesis via Symbolic Analysis (2016, methodology): “Various automated repair tools, such as GenProg [14], PAR [21], reliﬁx [39], Sem-Fix [26], Nopol [8], DirectFix [24] and SPR [23], to name only a few, have been introduced recently.”
CODIT: Code Editing With Tree-Based Neural Models (2018, methodology): “In generate and validate approaches, candidate patches are first generated and then validated by running test cases [52], [55], [86], [87], [88].”
DLFix: Context-based Code Transformation Learning for Automated Program Repair (2020, methodology): “Anti-patterns were integrated in existing search-based APR tools [38] (namely, GenProg [9] and SPR [23]) to help alleviate the problem of incor rect or incomplete fixes.”
DeepDelta: learning to repair compilation errors (2019, background): “Previous research in the area of automated program repair has focused on � nding patches when a test failure occurs through � xed templates and search-based techniques [16, 24, 28, 29].”
CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generation (2023, methodology): “For automatic code repairing, the semantic-based repairing techniques develop with the help of a specification for the intended program behavior (Nguyen et al., 2013b; Weimer et al., 2009; Long and Rinard, 2015; Qi et al., 2014).”
TBar: revisiting template-based automated program repair (2019, methodology): “Synthesis-based APR systems [37, 71, 72] explored to limit the search space on conditional bug fixes by synthesizing new conditional expressions with variables identified from the buggy code.”