Rohan Nayak

Ilia Kuznetsov, Rohan Nayak, Alla Rozovskaya et al.

Peer review is at the heart of modern science. As submission numbers rise and research communities grow, the decline in review quality is a popular narrative and a common concern. Yet, is it true? Review quality is difficult to measure, and the ongoing evolution of reviewing practices makes it hard to compare reviews across venues and time. To address this, we introduce a new framework for evidence-based comparative study of review quality and apply it to major AI and machine learning conferences: ICLR, NeurIPS and *ACL. We document the diversity of review formats and introduce a new approach to review standardization. We propose a multi-dimensional schema for quantifying review quality as utility to editors and authors, coupled with both LLM-based and lightweight measurements. We study the relationships between measurements of review quality, and its evolution over time. Contradicting the popular narrative, our cross-temporal analysis reveals no consistent decline in median review quality across venues and years. We propose alternative explanations, and outline recommendations to facilitate future empirical studies of review quality.

Masoud Bozorgi, Rohan Nayak, Arslan Zaffar et al.

This literature focuses on doing a comparative analysis between Modular Audio Recognition Framework (MARF) and the General Intentional Programming System (GIPSY) with the help of different software metrics. At first, we understand the general principles, architecture and working of MARF and GIPSY by looking at their frameworks and running them in the Eclipse environment. Then, we study some of the important metrics including a few state of the art metrics and rank them in terms of their usefulness and their influence on the different quality attributes of a software. The quality attributes are viewed and computed with the help of the Logiscope and McCabe IQ tools. These tools perform a comprehensive analysis on the case studies and generate a quality report at the factor level, criteria level and metrics level. In next step, we identify the worst code at each of these levels, extract the worst code and provide recommendations to improve the quality. We implement and test some of the metrics which are ranked as the most useful metrics with a set of test cases in JDeodorant. Finally, we perform an analysis on both MARF and GIPSY by doing a fuzzy code scan using MARFCAT to find the list of weak and vulnerable classes.

Zinia Das, Mohammad Iftekharul Hoque, Renuka Milkoori et al.

This report focuses on improving the internal structure of the Distributed Modular Audio recognition Framework (DMARF) and the General Intensional Programming System (GIPSY) case studies without affecting their original behavior. At first, the general principles, and the working of DMARF and GIPSY are understood by mainly stressing on the architecture of the systems by looking at their frameworks and running them in the Eclipse environment. To improve the quality of the structure of the code, a furtherance of understanding of the architecture of the case studies and this is achieved by analyzing the design patterns present in the code. The improvement is done by the identification and removal of code smells in the code of the case studies. Code smells are identified by analyzing the source code by using Logiscope and JDeodorant. Some refactoring techniques are suggested, out of which the best suited ones are implemented to improve the code. Finally, Test cases are implemented to check if the behavior of the code has changed or not.