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University Calendar
Events for April
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PhD Defense - Anandi Hira
Mon, Apr 27, 2020 @ 10:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
Ph.D. Defense - Anandi Hira
Mon, April 27, 2020
10:00 am - 12:00 pm
Location: https://usc.zoom.us/j/92966727414
Title:
Calibrating COCOMO(R) II for Functional Size Metrics
PhD Candidate: Anandi Hira
Date, Time, and Location: Monday, April 27, 2020 at 10am on https://usc.zoom.us/j/92966727414
Committee: Dr. Barry Boehm, Dr. Shang-hua Teng, Dr. Bherokh Khoshnevis
To date, a generalizable effort estimation model with functional size metrics does not exist. This dissertation provides a generalizable effort estimation model by calibrating the COCOMO II model (a generalizable model that uses lines of code as size input) to use either IFPUG (FPs) or COSMIC Function Points (CFPs) directly as size parameters. The calibrated COCOMO II model estimated within 25% of the actuals 68% of the time for FPs and 70% of the time for CFPs. In comparison, the best of the alternative solutions provided estimates within 25% of the actuals 36% of the time for FPs and 38% of the time for CFPs.
FPs and CFPs have been found to work well in different scenarios: FPs are well-suited for Management Information Systems (MIS) or data-driven applications, while CFPs are also well-suited for embedded, real-time, and web applications. No empirical studies have attempted to characterize software attributes and how FSMs behave differently with respect to them. Five types of software attributes were identified in the datasets used for this dissertation based on the number and complexity of operations and algorithms. The results show that the correlation between FPs/CFPs and effort depends on the amount of complexity operations required with respect to the functional processes.WebCast Link: https://usc.zoom.us/j/92966727414
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Defense -
Thu, Apr 30, 2020 @ 10:00 AM - 01:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Detecting SQL Antipatterns in Mobile Applications
PhD Candidate: Yingjun Lyu
Committee:
William GJ Halfond (Chair)
Neno Medvidovic
Chao Wang
Jyo Deshmukh
Sandeep Gupta
Local databases underpin important features in many mobile applications. However, bad programming practices of using database operations, also called SQL antipatterns, can introduce high resource consumption, affect the responsiveness, and undermine the security of a mobile application.
In my dissertation, I designed and evaluated a framework, called SAND, to detect SQL antipatterns effectively and efficiently in mobile apps. The framework abstracts away the interactions between the application and the database. It provides a language that allows the framework users to query abstractions of application-database relationships and specify SQL antipattern detection tasks. To determine what kinds of application-database relationships should be abstracted, I first conducted a systematic literature review to collect a comprehensive list of SQL antipatterns and their detection approaches. I then analyzed the collected detection approaches and derived the abstractions from them. In order to extract the abstractions from the database access code, I developed a range of static analysis techniques that can analyze the database access code effectively and efficiently. Using experiments on the framework implementation for Android, I showed that SAND can be used to compactly (in 12-74 lines of code) specify SQL antipattern detection tasks previously reported in the literature. These detectors built on top of SAND precisely identified thousands of instances of SQL antipatterns with a precision of at least 99.4%. These detectors were also fast as applying eleven detectors only took an average of forty-one seconds per app. Overall, these results are positive and indicate that my framework can detect all kinds of SQL antipatterns effectively and efficiently in mobile apps.
WebCast Link: https://usc.zoom.us/j/94586333967
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
