CPT206 Computer Programming for Financial Mathematics:
Coursework 1 Task Specification
This is the specification task sheet for the Coursework 1 assessment component of your CPT206 module. The task covers all Learning Outcomes, and has a weighting of 15% towards the final grade for this module. For this assessment, you will solve one practice exercise of your choice each week in Weeks 3, 6, 9, and 12, and report on your solution. Your task is described precisely in Section 1. Detailed submission instructions are provided in Section 2.
1 Task description
In Weeks 3, 6, 9, and 12, a small number of practice exercises will be uploaded to the Learning Mall (section “CW1 materials”, Quiz “Week X exercises”). In each of these weeks, you should select one of these exercises to solve (four exercises in total), and write a report on your solution (four reports in total). The solution should be submitted to the Learning Mall Quiz, with the code and accompanying report submitted to the dedicated Learning Mall assignment page (see Section 2 for details). The report should consist of the following three sections.
1. Code explanation (15 marks). In this section you should provide a brief explanation of the different parts of your code, and detail how they meet the requirements as laid out in the exercise specification on Learning Mall.
2. Code development and testing (40 marks) . There are no limitations on tools used to help you develop the code solution. In particular, the use of generative AI is permitted for code development. In this section of the report, you should explain how you solved the coding exercise, including any use of generative AI tools or others (for example, by including screenshots of your conversation with XipuAI). You must however ensure that you have a full understanding of your code solution, so as to be able to explain it satisfactorily in Section 2 above. In this section, you should also detail the testing you performed on your code. This should include some test cases not provided in the Learning Mall exercise. Any debugging that was needed to fix your code should also be included in this section. For example, if your code initially failed to pass some of the hidden tests on Learning Mall, explain here how you solved the issue(s).
3. Personal reflection (20 marks). Finally, your report should include a personal reflection on your learning experience through completing the coding exercise. Questions that you may wish to consider could include, but are certainly not limited to, the following. What knowledge did you gain? How did solving the exercise improve your programming skills? If desirable, you may refer to specific objectives of that week’s lecture or the wider aims and learning outcomes of the course itself.
Your report should be typed into e.g. a Word document, using single line spacing and a size 12pt font. If including small pieces of code to demonstrate specific aspects , you may wish to distinguish these from your report writing by using a Monospaced font such as Courrier or similar. The total length of the report should not exceed two pages.
2 Submission instructions
In the dedicated “Coursework 1 submission” Assignment activity on the Learning Mall in Weeks 3, 6, 9, and 12, you should submit the following two files:
• Your report, converted to a PDF file, named as “CPT206 CW1 Week{X} {StudentID}”.
• The source code of your solution, in a “.java” file (or “.py” for Week 12). Keep the same file name as you used when developing the solution. The formatting and contents of your code should be identiical to the final version submitted to the Learning Mall. Marks will be awarded not just for correctness of the code, but also for code quality (25 marks in total for code).
You will also submit the actual code solution to the Learning Mall weekly practice exercise Quiz. For each week, the submission deadline is Sunday, at 23:59 (China-time), of that week of teaching. So for example, for the Week 3 report, the deadline is Sunday, 9 April, at 23:59 (China-time). No late submissions will be accepted.
While the use of various tools, including generative AI, is permitted in the development of the code solution, the report must be individual work. Plagiarism (e.g. copying materials from other sources without proper acknowledgement) is a serious academic offence. Plagiarism and collusion will not be tolerated and will be dealt with in accordance with the University Code of Practice on Academic Integrity. Submitting work created by others, whether paid for or not, is a serious offence, and will be prosecuted vigorously. The use of generative AI for content generation is not permitted in the report, other than the code solution presented in Section 1. Such a use would be considered in breach of the University Code of Practice on Academic Integrity, and dealt with accordingly.