2014 IIAI 3rd International Conference on Advanced Applied Informatics 2014
DOI: 10.1109/iiai-aai.2014.139
|View full text |Cite
|
Sign up to set email alerts
|

A Method for Detecting Bad Smells and ITS Application to Software Engineering Education

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
5
0
1

Year Published

2016
2016
2019
2019

Publication Types

Select...
3
3

Relationship

0
6

Authors

Journals

citations
Cited by 6 publications
(6 citation statements)
references
References 4 publications
0
5
0
1
Order By: Relevance
“…However, detecting bad smells by using software metrics is subject to interpretation and it is usually imprecise [20,31]. Moreover, metrics‐based tools are not able to detect some errors that depend on the program structure or semantic (such as a public field that is breaking the object encapsulation) [12,20].…”
Section: Related Workmentioning
confidence: 99%
“…However, detecting bad smells by using software metrics is subject to interpretation and it is usually imprecise [20,31]. Moreover, metrics‐based tools are not able to detect some errors that depend on the program structure or semantic (such as a public field that is breaking the object encapsulation) [12,20].…”
Section: Related Workmentioning
confidence: 99%
“…So when we works with programming modules than it create issue to detection and refactoring bad smells from a large code. A graphical view is missing of clustering of various modules while performing detection and refactoring [14]. Smell detection tools are currently not useful to review which sections of code required to be enhanced.…”
Section: Related Workmentioning
confidence: 99%
“…The SQL injection validation is compared to both parse trees. Yuki Ito et al [10] proposed a method for detecting the factors behind bad smells by using declarative meta programming (DMP). The detection is performed by comparing bad smells with the abstract syntax tree (AST) of the target source code and then representing the comparison using Prolog.…”
Section: Related Workmentioning
confidence: 99%