[Eecs_msee] EECS Research Seminar Talk-Exploring the Software Quality Maze: Detecting Scattered and Tangled Quality Concerns in Source Code to Aid Maintenance Tasks

Tue Mar 26 15:50:39 EDT 2024

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Rrezarta Krasniqi, Ph.D.
University of North Texas

[cid:image004.jpg at 01DA7F95.57E6F760]Biography:
Rrezarta Krasniqi recently earned a Ph.D. in the Department of Computer Science
and Engineering at the University of North Texas. Her research interests lie in
Software Engineering, specifically Requirements Engineering, with an emphasis on
Software Maintenance and Evolution. In her research, she primarily focuses on
problems related to the detection of quality-related bugs. She develops tools and
techniques to enhance and contextualize the understanding of complex quality
issues related to security, usability, reliability among others that emerge as byproducts
of long-term maintenance efforts. Her research has resulted in 12 firstauthored
conference proceedings and 2 first-authored journal articles, among
others. One of her recent papers has been selected as a top best paper in SANER'21
and was invited and accepted for publication in the prestigious EMSE'23 Journal. Last year, she has been
awarded a doctoral research grant to further support her doctoral studies and received the best poster award at
the Tapia'23 conference. Her work has also been featured in the public press for receiving a People's Choice
Award in the 3MT Doctoral Competition and for being selected as a finalist for the NCWIT Collegiate Award.
Currently, she is serving as a Program Committee member for ICSE, SANER, CAIN, and EASE, including as
a reviewer for the Software Quality Journal and the Journal for Software: Evolution and Process. She holds a
B.S. degree in Computer Science and Mathematics from the University of Prishtina, an M.S. degree in Computer
Science from Midwestern State University, and an M.S. degree in Computer Science and Engineering from the
University of Notre Dame.
Abstract:
Exploring the Software Quality Maze: Detecting Scattered and Tangled Quality Concerns in Source
Code to Aid Maintenance Tasks
Software quality attributes, such as reliability, security, and usability, are often well-defined and understood at
the requirement level. They lay the ground foundation necessary to achieve high-quality, robust, user-friendly,
and trustworthy software systems. However, when addressing these attributes at the code level, two significant
challenges emerge. First, they tend to scatter across the codebase due to improper encapsulation of objectoriented
classes, hampering the visibility of quality-related components across the codebase. Second, they
become tangled within a single module due to intricate interdependencies with functional aspects of the code.
Addressing quality concerns in the presence of scattered and tangled code can lead to unforeseen issues. For
example, software developers may inadvertently introduce new and latent bugs or incorrectly implement code
components deviating from the original system-wide requirements. To tackle these pressing issues, my research
proposes a series of state-of-the-art solutions integrating ML-based techniques and NLP-based techniques,
including static program analysis techniques to automatically and effectively detect, repair, and untangle quality
concerns present at the code level.
In this talk, I will discuss two recent projects on this topic. First, I will introduce a multi-layered framework
that combines Machine Learning, Information Retrieval, and Natural Language Processing that automatically
detects several intertangled quality-related issues present in bug report code descriptions. Next, I will present a
context-aware bug fixing framework that can cluster scattered quality concerns into a meaningful hierarchy,
inferring a set of candidate classes relevant for recommending quality bug repairs. Finally, I will discuss the
future research plan, where my focus will be on capturing the evolving nature of quality concerns during
software maintenance and evolution, aiming to advance intelligent tools that enhance human capabilities. By
capturing contextual information from various artifacts, task dependencies, and historical data, my long-term
goal is to improve the productivity of knowledge workers, including software developers.

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