This Fall 2020 semester, I am teaching an advanced topics seminar on the idea of Modularity in Software Engineering. The goal of this seminar is to discuss some classic and recent papers on modularity in software engineering and programming languages.

PhD student Rangeet Pan and Hridesh Rajan have received an ACM SIGSOFT Distinguished Paper Award at the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) 2020 for their work “On Decomposing a Deep Neural Network into Modules”. So far deep neural networks, the machine learning models used for deep learning, are thought of as monolithic machine learning models. This work shows for the first time that it is possible to decompose a deep neural network into parts so that these parts can be reused to create another deep neural network and replaced to improve the machine learning model.

Distinguished papers are given to at most 10% of the papers accepted at an ACM sponsored conference. ESEC/FSE is an internationally renowned forum for researchers, practitioners, and educators to present and discuss the most recent innovations, trends, experiences, and challenges in the field of software engineering. Along with the International Conference on Software Engineering (ICSE), it is widely recognized as the top-2 conferences in software engineering. ESEC/FSE brings together experts from academia and industry to exchange the latest research results and trends as well as their practical application in all areas of software engineering.

The EAPLS Best Paper Award 2020 is awarded to the paper “An Empirical Study on the Use and Misuse of Java 8 Streams”, by Raffi Khatchadourian, Yiming Tang, Mehdi Bagherzadeh (Ph.D. ‘16 computer science) and Baishakhi Ray.

Md Johirul Islam has successfully defended his PhD thesis entitled “Towards Understanding the Challenges Faced by Machine Learning Software Developers and Enabling Automated Solutions”. The abstract of the thesis is as follows:

This work presents takes the first step toward decomposing a monolithic deep neural network (DNN) into modules so that components of the deep neural network can be reused to create another network. The approach also takes the first step towards enabling replacement of features/concerns within the DNN.

This work presents a comprehensive study of machine learning models to understand whether they exhibit bias.

As researchers and public-health experts join forces to battle COVID-19, computer scientists from Iowa State University have developed a data science infrastructure that will drastically improve research efficiencies for scientists who study the novel coronavirus.

This work proposes bespoke control flow analysis (bcfa), a novel source code analysis technique for performing large scale source code analysis over the control flow graphs.

This work presents a comprehensive study of bug repair patterns for five DNN libraries

Samantha-Syed Khairunnesa has successfully passed the PhD preliminary exam. The proposal abstract: