‌COMP0034 2023/24 Coursework 2 specification (Individual)

Version: 1 released 06/02/2024

‌Contents

1. Introduction

2. Getting started

3. Technologies that must be used

4. Coursework content

5. Submission

6. Marking

7. Appendices

   1. Adding challenge

   2. Unresolved bugs and code issues

   3. Referencing

   4. Support and guidance

   5. Changes to coursework submission dates

   6. SoRA and EC

   7. Late submission penalties

   8. Data sets

1. ‌Introduction

   1. Application code

   2. Test code

   3. Tools and techniques

2. ‌Getting started

   1. Create a GitHub repository using GitHub classroom:

      1. Login to GitHub.com.

      2. Click on the GitHub classroom link for individuals.

      3. Accept the assignment.

      4. If prompted, accept to join the comp0035-ucl organisation.

   2. Make your data available in the project.

      You can use any of the following approaches to access the data in your app:

      dataset .csv/.xlsx file

      REST API from COMP0034 coursework 1 SQLite database

3. ‌Technologies that must be used

   1. GitHub for source code control.

      Use GitHub, with repositories created in GitHub classroom to allow tutors and PGTAs to gain access. If you can’t use GitHub for some reason please contact the course tutor to agree alternative source code control.

   2. Python coding environment.

      Use a Python coding environment such as Visual Studio Code or PyCharm Professional. Jupyter notebooks is not accepted for the coursework.

   3. Flask python library or Plotly Dash python open source.

      The course will teach Flask and Plotly Dash. You may use Streamlit instead of Dash however this will not be taught in the course. You must not use Django, React or any other web framework. You may use additional Python libraries to support your app code such as Flask-SQLAlchemy, Flask-WTF, requests, etc.

   4. Use .pdf or .md format for written content.

      All written evidence must be combined into a single file, either PDF or markdown.

4. ‌Coursework content

   ‌Application code‌

   Write code to create a web app that makes use of your data.

   ML apps should have at least 2 pages, one of which interacts with the ML model. ‘Other’ apps should have at least 3 pages that interact with data.

   Interacts with the data using one of the following methods: SQLite database using Flask-SQLAlchemy

   REST API using Python requests

   .csv/.xlsx using pandas DataFrame

   Responsive design using open source CSS template.

   Note: Writing your own CSS will not be considered in the marking so is not recommended. JavaScript is not considered in the marking and should not be needed.

   If you have code that is not working as desired, or wish to explain anything related to your app, please add a section called ‘Application code’ to your comp0034-coursework2.(pdf/.md). For errors/issues please describe:

   what the problem is or how it is affecting your app where in the code the issue is

   what steps you took to try and address the issue

   ‌Test code‌

   Write integration tests for the app using pytest and selenium webdriver.

   Add any supporting evidence (i.e. anything that is not code) to comp0034-coursework2. (pdf/.md).

   The minimum required:

   Uses pytest and selenium webdriver (for Chrome)

   A test that verifies the home page URL can be accessed

   Two tests that carry out sequences of actions the user of your app would expect to perform

   ‌Use of tools and techniques

   Add a section called ‘Tools and techniques’ to your comp0034-coursework2.(pdf/.md).

   Provide evidence that demonstrates effective use of appropriate use of software engineering tools and techniques.

   The minimum required:

   Source code control:

   You must use GitHub for source code control unless otherwise agreed in advance with the course tutor.

   Add URL to your GitHub repository the evidence.

   Demonstrate use of source code control throughout the coursework. Appropriate use of .gitignore

   Set-up instructions:

   Add instructions to set up and run your app and your test code to comp0034- coursework2.(pdf/.md).

   ‌References should include:

   Acknowledgement of the use of AI Attribution for the data set

   Code references

   Books, papers, websites etc

   See the Appendix – Referencing for details on how to reference each of the above.

5. ‌Submission

   Submit your work on Moodle as a single .zip in the assignment submission. Refer to Moodle for the deadline date and time.

   Moodle is used as the submission date/time and to authenticate students. GitHub is not an accepted submission system.

   The .zip should expand to the correct folder structure with all required files. Ensure that all files are included in the zip, files that are only provided as URLs will be excluded from marking consideration (since they may be modified after the coursework is submitted).

   The submission must include:

   Application code

   Test code

   comp0034-coursework2.pdf or comp0034-coursework2.md. If using markdown make sure any linked files are also included in the submission.

6. ‌Marking

‌Mark allocation and calculation

An module is expected to take around 150 learning hours. Each coursework should take 20-25 hours.

While not exact, an indication of the effort given the marking weighting is shown below.

‌Grading criteria

The ‘UCL Computer Science: Marking Criteria and Grade Descriptors’ will be used to assess the coursework (using only the criteria that are relevant to this coursework). In addition to the

‌As a guide, marking considers aspects such as:

are the minimum requirements met?

range of skills/sophistication/challenge evidenced in the resulting application code code quality: Python standards, structure and configuration of the application, use of functions and/or classes, DRY and other design principles

‌Test code

As a guide, marking considers aspects such as:

did you meet the minimum requirements of this section? evidence of challenge and sophistication in the test code.

the quality and structure of the test code. This includes code quality, test naming and structure, documentation, appropriate use of assertions, use of fixtures etc.

‌ Has source code control been used regularly and appropriately?‌

Are the dependencies managed using an appropriate technique? Are all the dependencies included?

Are set up instructions provided? Was the app able to be run after the set-up instructions were followed?

These are not covered by the generic CS grading criteria.

‌Appendices‌

1. Adding challenge

2. Unresolved bugs and code issues

3. Referencing

4. Support and guidance

5. Changes to coursework submission dates

6. SoRA and EC

7. Late submission penalties

8. Data sets

1. ‌Adding challenge

   The following are suggestions of things you might do to increase the challenge of your solution. This is not an exhaustive list.

   ‌Application code

   A more challenging feature that demonstrates further skills/knowledge. This may be an additional feature though do not try to add many additional features as more features does not mean more marks! Focus on quality rather than quantity.

   (Dash) Use of callbacks and features that allow the user to tailor charts to their preferences

   Error handling

   ‌Tools and techniques‌

   Evidence of the configuration and use of GitHub Actions to run the tests

   Linting. There are also static and dynamic program analysis tools that do more than linting, you would need to research what these are and then apply them appropriately.

   Evidence that you used AI effectively to improve your code, rather than to just write the code for you e.g.

   to generate alternatives that you considered

   to improve the quality or efficiency of your code.

   To demonstrate this please include the prompts used (if applicable) and the code before/after AI.

   Note: If you use other tools/techniques please provide evidence incorporated into your comp0034-coursework2.(pdf/md). For example, include screenshots or image formats in your file. A hyperlink to an external file is not acceptable for proof of submission.

2. ‌Unresolved bugs and code issues

   There may be problems with your code that you were not able to address in the timescales of the project. This is not unusual either for the coursework or in real life.

   If this occurs, add the following to the comp0034-coursework1.(pdf/md):

   what the problem is or how it is affecting your app where in the code the issue is

   what steps you took to try and address the issue

3. ‌Referencing

   ‌Acknowledgement of the use of AI

   If you did not use AI then clearly state this. Otherwise, you must include the following details:

   Name and version of the generative AI system used; e.g. ChatGPT-3.5 Publisher (company that made the AI system); e.g. OpenAI

   URL of the AI system.

   Brief description (single sentence) of context in which the tool was used. Statement of how the AI influenced your code.

   This is based on UCL student guidance on the use of AI. |

   ‌Reference your dataset

   Include any details to acknowledge your dataset (attribution) to comply with any license condition required for your data set (given in the data set link in COMP0035 Moodle).

   ‌Book, journal, web article etc. references

   It is unlikely to you will have any but if you do then include them.

   ‌UCL has no single method for referencing code. For the purposes of this coursework use the following.‌

   Give the author, URL and the date of retrieval. If you adapted the code, indicate “Adapted from” or “Based on”.

   Include citations within your code using code comments close to the affected code.

   An example:

   def play_sound(button_text):

   # Adapted from code from ‘remdog’ on the Plotly Community Forum at

   # https://community.plotly.com/t/linking-scatter-plot-elements-to-audio- files/11908/6

   # Accessed 01/02/21

   …some code here…

   ‌When to cite code

   You must cite external sources; including where you copy and then adapt the code. External sources include:

   user forums e.g. stack overflow

   open source repositories e.g. GitHub repos where an explicit open source license is stated

   communities associated with a library e.g. plotly forum

   It is not appropriate to copy from other students on the course, past or present. For this coursework you do not need to cite the following external sources:

   course teaching materials

   official library documentation and tutorials of the python libraries and frameworks

4. ‌Support and guidance

   The assessments start in week 1 (coursework 1) and week 5 (coursework 2) of the course. You are expected to make progress each week.

   1. Moodle activities (online): There are weekly activities and checkpoints in Moodle to guide your progress.

   2. Weekly lab/tutorial sessions (in person): Module staff will be available to provide support and guidance in weekly lab/tutorial sessions. Use these sessions to complete activities and then work on your coursework.

   3. Tutor office hours (online): The tutor will be available during a weekly module office hour (see Moodle).

   4. Moodle Q&A (online): You can ask questions at any time in the Moodle Q&A forum where anyone in the course is able to reply. When posting questions about your code, please post the URL to your repository. This enables the tutor and PGTAs to see your code which is usually essential for solving technical problems! Other students won’t be

‌These have been planned carefully to fit with the teaching schedule.

Requests to change these for all students must be made to the course tutor who will then forward the request to the relevant authority in the computer science department. Requests must be supported with appropriate evidence of the need for change.

1. ‌SoRA and EC

   If you have an approved extension resulting from a SoRA or EC, these will be carefully checked by the teaching and learning team before marks are entered in portico.

   However, the deadlines in Moodle may not be modified for individuals, so it may appear on Moodle that your submission is late even though you have an approved extension to cover the period.

2. ‌Late submission penalties

   Late submission rules apply to all assessments.

   Any penalties for late submission are applied by the computer science teaching and learning team when marks are entered in portico.

   The mark you see in the Moodle gradebook may not be your final mark as it is the mark before moderation or any penalty has been applied.

3. ‌Data sets

Only use the ethics approved data sets.

The data sets allocated to students on this course have been approved by the Computer Science Ethics Committee and signed by the Head of Department for use in this course. These data sources comply with GDPR and UCL ethics and data protection policies. You must not use any other data set for the coursework.

The approved data sets do not require data protection registration and since you will not be working with participants in your project there is no requirement to complete any further training relating to data protection and GDPR.