Name
Abstract | ||
|---|---|---|
Code smell is an indication of a software designing problem. The presence of code smells can have a severe impact on the software quality. Smells basically refers to the structure of the code which violates few of the design principals and so has negative effect on the quality of the software. Larger the source code, more is its presence. Software needs to be reliable, robust and easily maintainable so that it can minimize the cost of its development as well as maintenance. Smells may increase the chances of failure of the system during maintenance. A SLR has been performed based on the search of digital libraries that includes the publications since 1999 to 2016. 60 research papers are deeply analyzed that are most relevant. The objective of this paper is to provide an extensive overview of existing research in the field of bad smells, identify the detection techniques and correlation between the detection techniques, in addition to find the name of the code smells that need more attention in detection approaches. This SLR identified that code clone (code smell) receives most research attention. Our findings also show that very few papers report on the impact of code bad smells. Most of the papers focused on the detection techniques and tools. A significant correlation between detection techniques has been calculated. There are four code smells that are not yet detected are Primitive Obsession, Inappropriate Intimacy, Incomplete library class and Comments. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-62404-4_49 | COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2017, PT V |
Field | DocType | Volume |
Programming language,Systematic review,Software engineering,Computer science,Source code,Computer network,Software,Digital library,Software quality,Code clone,Code smell,Java source code | Conference | 10408 |
ISSN | Citations | PageRank |
0302-9743 | 1 | 0.35 |
References | Authors | |
48 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aakanshi Gupta | 1 | 1 | 0.35 |
| Bharti Suri | 2 | 63 | 8.02 |
| Sanjay Misra | 3 | 118 | 26.58 |