26 citations · 33 across the 6 of their papers we have counts for
6 papers
An Empirical Study on Predictability of Software Code Smell Using Deep Learning Models
Himanshu Gupta, Tanmay G. Kulkarni, Lov Kumar +2
Code Smell, similar to a bad smell, is a surface indication of something tainted but in terms of software writing practices. This metric is an indication of a deeper problem lies w…
Empirical Analysis on Effectiveness of NLP Methods for Predicting Code Smell
Himanshu Gupta, Abhiram Anand Gulanikar, Lov Kumar +1
A code smell is a surface indicator of an inherent problem in the system, most often due to deviation from standard coding practices on the developers part during the development p…
A Comparative Study of Different Source Code Metrics and Machine Learning Algorithms for Predicting Change Proneness of Object Oriented Systems
Lov Kumar, Ashish Sureka
Change-prone classes or modules are defined as software components in the source code which are likely to change in the future. Change-proneness prediction is useful to the mainten…
Using Source Code Metrics and Ensemble Methods for Fault Proneness Prediction
Lov Kumar, Santanu Rath, Ashish Sureka
Software fault prediction model are employed to optimize testing resource allocation by identifying fault-prone classes before testing phases. Several researchers' have validated t…
A Bibliometric Study of Asia Pacific Software Engineering Conference from 2010 to 2015
Lov Kumar, Saikrishna Sripada, Ashish Sureka
The Asia-Pacific Software Engineering Conference (APSEC) is a reputed and a long-running conference which has successfully completed more than two decades as of year 2015. We condu…
Thirteen Years of Mining Software Repositories (MSR) Conference - What is the Bibliography Data Telling Us?
Lov Kumar, Ashish Sureka
The Mining Software Repositories (MSR) conference is a reputed, long-running and flagship conference in the area of Software Analytics which has successfully completed more than on…