The Computer Science Quiz Question Bank application is a web portal to allow sharing of T/F, Multiple Choice, Multi-select and Short Answer questions pertaining to first year programming concepts (Python and Java). Instructors can import or export content to the Question Bank from D2L or Canvas Learning Management Systems.
Stacey A. Koornneef, Jeremy Bradbury, Michael Miljanovic will demo the Run, Llama, Run educational game at the the 53rd ACM Technical Symposium on Computer Science Education (SIGCSE 2022) in Providence, Rhode Island, USA this month.
The paper “GidgetML: An Adaptive Serious Game for Enhancing First Year Programming Labs” by SEER Lab’s Michael Miljanovic and Jeremy Bradbury has been accepted for publication in the Software Engineering Education and Training (SEET) track of the 42nd International Conference on Software Engineering (ICSE 2020).
Continue reading “ICSE 2020 SEET Paper – “GidgetML: An Adaptive Serious Game for Enhancing First Year Programming Labs””SEER Lab’s Jude Arokiam and Jeremy Bradbury‘s paper “Automatically Predicting Bug Severity Early in the Development Process,” has been accepted for publication in the New Ideas and Emerging Results (NIER) track at the 42nd International Conference on Software Engineering (ICSE 2020). The paper uses the AutoBugTriager tool which is available as an open source project.
In February 2020, Michael Miljanovic and Jeremy Bradbury gave a presentation on “Educational Games for K-12 Computer Science” at the 20th Annual Conference of the Association for Computer Studies Education (ACSE 2020). ACSE 2020 is the largest conference for K-12 computing educators in Ontario, Canada.
In 2018, we published a review of 49 serious games for learning how to program:
For each game we assessed the programming content of the game using the ACM/IEEE-CS Joint Task Force on Computing Curricula: Computer Science Curricula 2013. We also assessed how each game was evaluated both in terms of the research methods used (e.g., surveys, formal interviews, skill tests, etc.) and the research questions asked:
Any system where erroneous behaviour can lead to serious injury or a potential loss of life is classified as a safety critical system. This is true for self-driving or autonomous vehicles where a vehicle malfunction can lead to the injury or death of the driver, passengers or others outside the vehicle. The potential for injury or death is why it is paramount that the developers of self driving vehicles ensure the systems works safely before deploying them to users on public roads. In the field of self-driving vehicles, it is not clear if this best practice is always being followed. While self-driving vehicles are testing extensively using computer simulation and closed circuit test tracks, they are also tested on public roads. For example, driver assistance systems like Tesla’s Autopilot have been beta-tested by real users. Fully autonomous vehicles such as Uber’s self-driving car have also been tested outside of controlled settings on public roads. In cases where testing occurs in public, the vehicle-under-test is surrounded by pedestrians and drivers who may be completely unaware that their interaction is helping to test and improve an autonomous vehicle. This was the case on March 18, 2018, in Tempe, Arizona when Uber’s self-driving car, with a human driver present, hit and killed a pedestrian (see SFGate).
More recently, news reports highlighting crashes of Tesla vehicles with semi-autonomous and autonomous capabilities have again brought the issues of safety and testing to the forefront. “Three crashes involving Teslas that killed three people have increased scrutiny of the company’s Autopilot driving system, just months before CEO Elon Musk has planned to put fully self-driving cars on the streets” (see CBC). It is important to note that unlike Uber’s self-driving car, a Tesla with Autopilot is not a fully autonomous driving system – “…Autopilot is a hands-on driver assistance system that is intended to be used only with a fully attentive driver. It does not turn a Tesla into a self-driving car nor does it make a car autonomous” (see Tesla website). The two main features of Autopilot are Traffic-Aware Cruise Control, for matching the vehicle’s speed to traffic, and Autosteer, for steering within a specific lane. Navigate on Autopilot is a Tesla beta feature which autonomously controls lane changes and highway interchange and ramp navigation.
Continue reading “Safety, Testing and Self-Driving Cars”
“Y2K is a great case study that we can use to talk about best practices for how we develop software today.” SQR Lab leader Prof. Jeremy Bradbury was interviewed recently by Aaron Streck of Global News Durham for the 20th anniversary of the Y2K (Millennium) bug. You can watch the news segment and read the article here.
On May 3, 2018 I gave an invited talk at the 9th Annual Graduate Student Research Conference at UOIT. The topic of my talk was “How to Succeed (and Fail) at Interdisciplinary Research.”
Interdisciplinary research is defined as research that involves multiple areas of knowledge and expertise.
As graduate students, researchers are often trained to develop expertise in only one specific area and those interested in interdisciplinary problems usually need to collaborate to be successful.
In this talk I shared my own experiences with interdisciplinary research and I highlighted my own observations on how to both succeed and fail at research that spans multiple knowledge areas. Challenges with interdisciplinary communication, collaboration, funding, publishing and reviewing were all explored. To find out more please see my presentation slide deck.
Continue reading “How to Succeed (and Fail) at Interdisciplinary Research”This week I gave a research seminar at Dalhousie University and at Mount Allison University on “Automating Software Development Using Artificial Intelligence (AI).” The intersection of AI and Software Engineering is an active research area and has lead to a number of effective and novel applications of machine learning, metaheuristic algorithms and deep learning. Many of these applications of AI to software development can be categorized as:
Not all Software Engineering research problems can be suitably addressed by AI techniques. A good first step to determine if a given software development problem can be addressed with AI is to see if it can be re-framed in terms of optimization, classification, prediction, etc. That is, can it be re-framed in terms of the type of problems that AI methods are effective at solving?
To find out more about the Software Quality Research Lab‘s work in this area please see the abstract and slides from my talk below.
Continue reading “Automating Software Development Using Artificial Intelligence”