My first-author paper named “An Empirical Study of Architectural Decay
in Open-Source Software” will be appeared in the Proceeding of the IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ARCHITECTURE (ICSA 2018)
Abstract – Architecture is the set of principal design decisions about a software system. In practice, new architectural decisions are added and existing ones reversed or modified throughout a system’s lifetime. Frequently, these decisions deviate from the architect’s well-considered intent, and software systems regularly exhibit increased architectural decay as they evolve. The manifestations of such ill-considered design decisions are seen as “architectural smells”. To date, there has been no in-depth study of the characteristics or trends involving this phenomenon. Instead, when referring to architectural smells and their negative effects, both researchers and practitioners had to rely on folklore and their personal, inherently limited experience. In this paper, we report on the systematic step we have taken in investigating the nature and impact of architectural smells. We have selected a set of representative architectural smells from literature and analyzed their instances in 421 versions from 8 open-source software systems. We have (1) developed algorithms to automatically detect instances of multiple architectural smell types, and (2) analyzed relationships between the detected smells and the lists of issues reported in the systems’ respective issue trackers. Our study shows that architectural smells have tangible negative consequences in the form of implementation issues as well as code commits requiring increased maintenance effort throughout a system’s lifetime.
My third-author paper named “Recovering Architectural Design Decisions” will be appeared in the Proceeding of the IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ARCHITECTURE (ICSA 2018)
Abstract – Designing and maintaining a software system’s architecture typically involve making numerous design decisions, each potentially affecting the system’s functional and nonfunctional properties. Understanding these design decisions can help inform future decisions and implementation choices and can avoid introducing regressions and architectural inefficiencies later. Unfortunately, design decisions are rarely well documented and are typically a lost artifact of the architecture creation and maintenance process. The loss of this information can thus hurt development. To address this shortcoming, we develop RecovAr, a technique for automatically recovering design decisions from the project’s readily available history artifacts, such as an issue tracker and version control repository. RecovAr uses state-of-the-art architectural recovery techniques on a series of version control commits and maps those commits to issues to identify decisions that affect system architecture. While some decisions can still be lost through this process, our evaluation on Hadoop and Struts, two large open-source systems with over 8 years of development each and, on average, more than 1 million lines of code, shows that RecovAr has the recall of 75% and a precision of 77%. Our work formally defines architectural design decisions and develops an approach for tracing such decisions in project histories. Additionally, the work introduces methods to classify whether decisions are architectural and to map decisions to code elements. Finally, our work contributes a methodology engineers can follow to preserve design-decision knowledge in their projects.
Finished my internship on Aug 18th, 2017. It’s time to go back to LA.
It’s my pleasure to be at VERITAS Cutting Edge 2016 and present my work during the summer
Picture: With Bill Coleman, CEO of Veritas, and other interns
I had started an internship at Veritas Technologies LLC (Culver city office) since May 2016. I will propose an approach to apply NLP and ML to suggest email retention rules.