Measuring the impact of lexical and structural inconsistencies on developers’ cognitive load during bug localization

On the Documentation of Refactoring Types

“…Although our approach attempted to thoroughly predict method-level refactoring types, several inconsistency types between source code and documentation might occur. Several studies (Arnaoudova et al, 2016;Fakhoury et al, 2019b;Kim & Kim, 2016) have identified and detected recurring poor practices related to inconsistencies among the documentation and implementation of the code elements. Because such inconsistencies can affect software comprehensibility and maintainability, this research question aims at exploring the frequency of different inconsistency types that might help in reporting any early inconsistency between refactoring types detected by refactoring detector tools and their documentation.…”

“…Previous studies investigated the case when there is a disagreement between source code and its documentation in the context of programming misconception (Swidan et al, 2018), linguistic anti-patterns (Arnaoudova et al, 2016), bug localization (Fakhoury et al, 2019b), and code review (Ebert et al, 2021). In their study on misconceptions in programming education for school students, (Swidan et al, 2018) observed that younger learners hold common programming misconceptions that cause them to make errors.…”

“…Further, (Arnaoudova et al, 2016) investigated developers' perception of linguistic anti-patterns and developed a catalog of 17 types of linguistic anti-patterns related to inconsistencies, findings that the majority of the participants perceive linguistic anti-patterns as poor practices and must be avoided. (Fakhoury et al, 2019b) showed that inconsistencies in the source code have a significant effect on cognitive load, success, and time spent on program comprehension. More recently, (Ebert et al, 2021) discussed how developers deal with confusion in code reviews caused by unclear commit messages and lack of documentation.…”

Ten simple rules on writing clean and reliable open-source scientific software

“…Existing institutional knowledge and growing community practice standards serve as evidence of their efficacy. Adopting clean code practices helps to standardize and organize software code in order to enhance readability and reduce cognitive load [ 16 , 17 ] for both the initial developer and downstream contributors [ 18 ]. Increasing readability while reducing cognitive load allows developers to concentrate on core functionality and reduce errors, while also exemplifying clean and inviting code for community open-source contributors.…”

“…There are additional broad guidelines for designing useful unit tests, but, to some degree, appropriate unit tests demand both creativity and subject matter knowledge on the part of the programmer. Basic prescriptions that apply to any codebase include straightforward test naming conventions that reduce coders’ cognitive load [ 17 ] and the use of specialized environments, called fixtures, to standardize test inputs and isolate targeted behavior from other dependencies [ 19 ]. As with coding style, consistency in unit test design is paramount.…”