相关论文: Nalin: Learning from Runtime Behavior to Find Name…

Consistency is one of the keys to maintainable source code and hence a successful software project. We propose a novel method of extracting the intent of programmers from source code of a large project (~300kLOC) and checking the semantic…

Concise and meaningful method names are crucial for program comprehension and maintenance. However, method names may become inconsistent with their corresponding implementations, causing confusion and errors. Several deep learning…

Proper naming of methods can make program code easier to understand, and thus enhance software maintainability. Yet, developers may use inconsistent names due to poor communication or a lack of familiarity with conventions within the…

Misleading method names in software projects can confuse developers, which may lead to software defects and affect code understandability. In this paper, we present DeepName, a context-based, deep learning approach to detect method name…

Method names play an important role in communicating the purpose and behavior of their functionality. Research has shown that high-quality names significantly improve code comprehension and the overall maintainability of software. However,…

Naming is very important in software development, as names are often the only vehicle of meaning about what the code is intended to do. A recent study on how developers choose names collected the names given by different developers for the…

Natural language elements in source code, e.g., the names of variables and functions, convey useful information. However, most existing bug detection tools ignore this information and therefore miss some classes of bugs. The few existing…

Developers routinely work with source files whose variable names are generic or misleading, and with teams moving quickly, many functions are left undocumented. This slows comprehension, increases the risk of subtle bugs, and makes it…

Data annotation plays a crucial role in ensuring your named entity recognition (NER) projects are trained with the right information to learn from. Producing the most accurate labels is a challenge due to the complexity involved with…

The label quality of defect data sets has a direct influence on the reliability of defect prediction models. In this study, for multi-version-project defect data sets, we propose an approach to automatically detecting instances with…

Many programming language techniques for incremental computation employ programmer-specified names for cached information. At runtime, each name identifies a "cache location" for a dynamic data value or a sub-computation; in sum, these…

Program code contains functions, variables, and data structures that are represented by names. To promote human understanding, these names should describe the role and use of the code elements they represent. But the names given by…

A key requirement for supervised machine learning is labeled training data, which is created by annotating unlabeled data with the appropriate class. Because this process can in many cases not be done by machines, labeling needs to be…

Method names are crucial to program comprehension and maintenance. Recently, many approaches have been proposed to automatically recommend method names and detect inconsistent names. Despite promising, their results are still sub-optimal…

Unknowingly, identifiers in the source code of a software system play a vital role in determining the quality of the system. Ambiguous and confusing identifier names lead developers to not only misunderstand the behavior of the code but…

Variable names are critical for conveying intended program behavior. Machine learning-based program analysis methods use variable name representations for a wide range of tasks, such as suggesting new variable names and bug detection.…

Token-inconsistency bugs (TIBs) involve the misuse of syntactically valid yet incorrect code tokens, such as misused variables and erroneous function invocations, which can often lead to software bugs. Unlike simple syntactic bugs, TIBs…

Nowadays, we are witnessing an increasing demand in both corporates and academia for exploiting Deep Learning (DL) to solve complex real-world problems. A DL program encodes the network structure of a desirable DL model and the process by…

The names of variables and functions serve as implicit documentation and are instrumental for program comprehension. But choosing good meaningful names is hard. We perform a sequence of experiments in which a total of 334 subjects are…

Large Language Models (LLMs) have successfully been applied to code generation tasks, raising the question of how well these models understand programming. Typical programming languages have invariances and equivariances in their semantics…