The document outlines a pre-synopsis presentation by a team from Amity School of Engineering focused on analyzing software design defects through machine learning, with an emphasis on detecting code smells that indicate poor design decisions. Key concepts include the identification of code smells, their causes, and the expected outcomes of using machine learning algorithms for their analysis. References to recent studies in the field are also provided.

1. Mentor:-
Ms. Aakanshi Gupta
AMITY SCHOOL OF ENGINEERING AND TECHNOLOGY,
NEW DELHI- 110061
An institution of Ritnand Balved Education Foundation (RBEF)
Branch: Computer Science and Engineering (CSE)
Pre-Synopsis Presentation
Team Members:-
Kritika Phulli [01610402716]
Nidhi Kumari Chauhan [02110402716]
Deepanshu Sharma [35210402716]
Vishal Bhat [41910402716]

2. Software Design Defects
And
Its Analysis
Using Machine Learning

3. Problem
Statement
Background
Of The Study
Expected
Outcome
References
Research
Design

4. Software
Engineering
SDLC

5. Design Issues
• Caused by Sub-optimal
design decisions which can
be identified in the source
code as code smells,
negatively impact the
software quality.
• Poor design decisions.

6. Code
Smells

7. Bad Smells
“Smell are symptoms of poor design or
implementation choices that causes long term
maintainability problem and mask bugs.”
The noticeable factors of code smell was
first designed by Fowler.
Some of the bad smells are listed below :
Member Ignoring Method Slow Loop
Leaking Inner Class Unclosed
Closable.

8. Research
Pathway
DETECTION • Initial focus is on detection of code
smells.
ANALYSIS
• Preparation of dataset after
rigorous study.
ML
TESTING
• Using machine learning
algorithms, to predict
the limiting criterias.

9. Expected
Outcome
• Type of smells
• Causes
• Limiting Criteria
Once Data is collected and Data sets are created,
We expect the outcomes emphasizing on :

10. References
• Thanis Paiva, Amanda Damasceno Eduardo Figueiredo and
Cláudio Sant’Anna, “On the evaluation of code smells and
detection tools”, Journal of Software Engineering Research
and Development 2017.
• A. Yamashita and L. Moonen, “Exploring the impact of inter-
smell relations on software maintainability: An empirical
study,” in Proceedings of the International Conference on
Software Engineering (ICSE). IEEE, 2018.

11. Thank You