Refactoring improving the design of existing code

By Mauricio A. Saca. 2017 IEEE 37th Central America and Panama Convention (CONCAPAN XXXVII), 2017

1,932 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/f69660326658b679c1e2ca65fa7d9a8d648ae240

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Citations in the corpus

When and Why Your Code Starts to Smell Bad (and Whether the Smells Go Away) (2017, neutral): “Their approach aims at discovering classes that really wants to support the interface of the superclass [29].”
Reinforcement Learning from Automatic Feedback for High-Quality Unit Test Generation (2023, neutral): “These sub-optimal design choices are commonly referred to as "test smells" [49], analogous to the concept of "code smells" for production code [19].”
Learning to Spot and Refactor Inconsistent Method Names (2019, methodology): “Given that the performance of identiﬁcation depends on the threshold value k representing the size of the sets of adjacent vectors (Lines 6 and 10 in Algorithm 1), we vary k as 1, 5, and n × 10 with n ∈ [1 , 10] .”
Structured Chain-of-Thought Prompting for Code Generation (2023, neutral): “The total generation length of two steps is the same as CoT prompting.”
A systematic literature review on agile requirements engineering practices and challenges (2015, background): “Code refactoring is meant to revisit and modify developed code structure, to improve on the structure and accommodate changes (Fowler, Beck, Brant, Opdyke, & Roberts, 2011).”
CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generation (2023, background): “Fowler (1999) first proposed the concept of code smells, introducing 22 types of code smells that violate design rules, along with their features and impacts.”
Accurate and Efficient Refactoring Detection in Commit History (2018, methodology): “Prete et al. [46, 62] developed a tool, Ref-Finder, which detects the largest number of refactoring types (63 of 72) from Fowler’s catalog [28].”
Exploration and Explanation in Computational Notebooks (2018, background): “The issues of notebook “cleanliness” and intelligibility resonate with the discussion of refactoring [8, 25] and “technical debt” in software engineering.”
Graph-Based Mining of In-the-Wild, Fine-Grained, Semantic Code Change Patterns (2019, background): “Note that the catalog of refactorings to design patterns [11], [12] represent good practice of code restructuring changes, e.g.,“Move Embellishment to Decorator” , “Replace Conditional with Polymorphism” .”
Bugram: Bug detection with n-gram language models (2016, methodology): “We consider a reported buggy sequence a refactoring issue based on principles of code smells proposed in [27], e.”
On the diffuseness and the impact on maintainability of code smells: a large scale empirical investigation (2017, methodology): “Bad code smells (also known as “code smells” or “smells”) were defined as symptoms of poor design and implementation choices applied by programmers during the development of a software project (Fowler 1999).”