A Static Code Smell Detector for SQL Queries Embedded in Java Code

Nagy, Csaba; Cleve, Anthony

doi:10.1109/scam.2017.19

Cited by 19 publications

(1 citation statement)

References 19 publications

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“…• 12% are found to target refactoring. Studies (Zhang et al, 2011;Yamashita and Moonen, 2012;Sjøberg et al, 2012;Fontana et al, 2013b;Hermans et al, 2016;Nagy and Cleve, 2017;Palomba et al, 2017;Di et al, 2018;Jain and Saha, 2021) enhanced the knowledge of refactoring. • 1% of the study i.e.…”

Section: Rq3-to Find the Most Deployed Technique To Study Code Smell ...mentioning

confidence: 99%

Study of Code Smells: A Review and Research Agenda

Tandon,

Kumar,

Singh

2024

Int. j. math. eng. manag. sci.

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Code Smells have been detected, predicted and studied by researchers from several perspectives. This literature review is conducted to understand tools and algorithms used to detect and analyze code smells to summarize research agenda. 114 studies have been selected from 2009 to 2022 to conduct this review. The studies are deeply analyzed under the categorization of machine learning and non-machine learning, which are found to be 25 and 89 respectively. The studies are analyzed to gain insight into algorithms, tools and limitations of the techniques. Long Method, Feature Envy, and Duplicate Code are reported to be the most popular smells. 38% of the studies focused their research on the enhancement of tools and methods. Random Forest and JRip algorithms are found to give the best results under machine learning techniques. We extended the previous studies on code smell detection tools, reporting a total 87 tools during the review. Java is found to be the dominant programming language during the study of smells.

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Section: Rq3-to Find the Most Deployed Technique To Study Code Smell ...mentioning

confidence: 99%

Study of Code Smells: A Review and Research Agenda

Tandon,

Kumar,

Singh

2024

Int. j. math. eng. manag. sci.

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A survey of problematic database code fragments in software systems

Alshemaimri

Elmasri

Alsahfi

et al. 2021

Engineering Reports

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Database code fragments exist in software systems by using Structured Query Language (SQL) as the standard language for relational databases. Traditionally, developers bind databases as backends to software systems for supporting user applications. However, these bindings are low‐level code and implemented to persist user data, so Object Relational Mapping (ORM) frameworks take place to abstract database access details. Both approaches are prone to problematic database code fragments that negatively impact the quality of software systems. We survey problematic database code fragments in the literature and examine antipatterns that occur in low‐level database access code using SQL and high‐level counterparts ORM frameworks. We also study problematic database code fragments in different and popular software architectures such as Service‐Oriented Architecture, Microservice Architecture, and Model View Controller. We create a novel categorization of both SQL schema and query antipatterns in terms of performance, maintainability, portability, and data integrity.

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