Software Engineering and Project Management methodologies in the Software Development Life Cycle (SDLC),
such as Behaviour Driven Development (BDD), Agile (Scrum and Kanban), and TOGAF.
Lessons learnt and key outcomes
Via the units mentioned below, I learnt to appreciate and apply the fundamentals of software engineering best practices and project management frameworks, critically assess their suitability throughout the SDLC and learn from case studies involving software engineering project-related failures to devise, manage, and deliver successful software engineering-related projects adopting robust and modern principles, best practices, and methodologies. These techniques involved appropriate definition of requirements as per the key user personas, tailored design to meet their prioritised needs and wants, robust design, software implementation and testing, gathering their early feedback, and enhancing the software throughout the SDLC, including via user acceptance testing (UAT). Furthermore, I learnt and applied various project management methodologies in traditional and modern SDLC models, such as TOGAF and Agile (both Scrum for delivery implementations and Kanban for fast-paced discovery-/hypothesis testing-related work), to build a working prototype of a remote-controlled race car-related game as a software application. In this team project, I learnt the importance of leveraging the team members' passion, skills, and expertise to design and deliver a software systems to meet key users' requirements whilst promoting diversity and inclusion in car racing, and complying with applicable data privacy regulations considering the storage and processing of required personal data, such as the European Union (EU)’s General Data Protection Regulations (GDPR).
Unit 1: Introduction to Software Engineering Project Management
Project management involves resource management, estimating the required work to deliver a software, and planning for its design, delivery, and support. Thus, it is crucial to have the required multi-disciplinary skillset and focus on execution by leveraging project management-related frameworks, such as Agile (Scrum, Kanban, or Scrumban), to support and accelerate modern software development projects. Such methodologies are more suitable than waterfall for large-scale client-focused software development and delivery, being centred on iteratively enhancing the product based on early feedback from end users. To understand and appreciate the importance of project management, case studies on software engineering-related project failures were explored. You can download my post as a part of a group discussion by clicking 'LEARN MORE' below.
Unit 2: Study: Why Projects Fail and Gathering Requirements Exercise
Gathering requirements as aligned as possible with the key user personas is key to ensure a software engineering-related project is set up for success. To this purpose, the Gherkin tool was introduced and further discussions were held on software engineering-related project failures introduced during the previous unit. You can download my responses to my peers by clicking ‘LEARN MORE’ below.
Unit 3: Estimating, Planning and Risk
In this unit, I learnt the art and science of project management in software engineering in further depth, particularly on estimating the required work appropriately whilst considering risks and dependencies, and actions to mitigate them. In this week, I had the chance to ponder on the discussion with my peers during the previous two units and provided a summary post, which can be downloaded by clicking ‘LEARN MORE’ below.
Unit 4: Risk Assessment and Estimating Tools
This unit enabled me to learn tools to assess risks and estimate the work to be carried as a part of a software engineering-related project within the Agile framework, with focus on Scrum. This methodology is useful in large-scale software project delivery and informed my summary post on recommending mitigating actions to the risks identified, which led to software engineering-related project failures reviewed during case studies over the previous units. You can read my contribution by clicking ‘LEARN MORE’ below and please click on 'More pages' once landed on GitHub at the bottom of the page to read the section entitled 'Risks and Dependencies' wherein I leveraged the Risks, Assumptions, Issues, Dependencies (RAID) model to outline risks that were ranked based on their degree of severity, which stem from interdependent requirements that may impact delivery negatively.
Unit 5: User Experience
This unit enabled me to learn and understand how important user experience is to ensure adoption of a system. In particular, I had the chance to ponder on the role and influence of the Project Manager in ensuring a smooth user experience from the first impression through to the support of users during the adoption of a system. In this unit, I contributed to a collaborative discussion with a post that you can download by clicking ‘LEARN MORE’ below, which focuses on further contextual elements and key users' expectations to account for to ensure a smooth user experience and drive adoption of a system.
Unit 6: pytest and Test-Driven Development
During this unit, testing technologies available in Python, such as the library 'pytest', were explored to support test-driven development (TDD). Such testing tools are essential to drive the development and delivery of high-quality, functional codes that can ensure a correct and smooth user experience, thus eliciting positive emotional responses and the subsequent adoption by the end users. You can access my pytest exercise by clicking ‘LEARN MORE’ below.
Unit 7: Software Development Life Cycles
In this unit, I had the chance to appreciate the role of the Project Manager throughout the SDLC and their influence to drive further user adoption by ensuring a smooth user experience, incorporating their feedback to enhance a system iteratively, in an agile manner. During this unit’s activity, I had the opportunity to ponder on the posts written by my colleagues and incorporated their complementary viewpoints into my summary post, which you can download by clicking ‘LEARN MORE’ below.
Unit 8: Python Data Structures
This unit allowed me to study and practise on creating and handling various data structures in Python, such as dictionaries (key-value pairs), lists, and sets (considering their intersections, unions, etc.). Such structures enable to store data appropriately for various purposes depending on the application considered. Furthermore, I had the chance to investigate and test more complex data structures, such as two-dimensional vectors (the 'Vector2' class) in the Python library named 'pygame', which you can view at the unit tests-related file 'tests/rc_car/test_car.py' by clicking ‘LEARN MORE’ below. Such two-dimensional vectors were leveraged to represent and update the position of the race car in the game's environment considering both x and y coordinates (embedded in a two-dimensional vector).
Unit 9: Quality Management Strategy
In this unit, I could learn and practise on how quality management strategies are crucial to a successful software and project delivery, including the appropriate use of linting tools or libraries in Python to ensure a high quality of the codes developed. In this instance, I had the chance to practise and appreciate how linting tools, such as the library 'pylint', can help to ensure a high code quality such that software can be consumed reliably by and is valuable to users. By clicking ‘LEARN MORE’ below, you can view my implementation of a GitHub Action to automate the linting check via pylint, as well as some improvements to the codes' style and docstrings to ensure the automated pylint-related code quality check on the GitHub Action I created passed (10/10 code quality).
Unit 10: Software Quality Monitoring in Python
Ensuring software quality involves iterative efforts and, thus, appropriate monitoring. To this purpose, various complementary libraries or tools in Python, such as the libraries pylint, pyflakes, pycodestyle, pydocstyle, were assessed and compared to support the development and delivery of high-quality code, thus accounting for different aspects, such as correct syntax and consistent style, as well as the presence of both classes' and methods' docstrings. You can access my hands-on assessment of various linting tools in Python by clicking ‘LEARN MORE’ below.
Unit 11: Software Engineering Project Management: Future Trends
Trends and advances in software engineering and related project management were considered in both academic research and industry, such as Artificial Intelligence, the Internet of Things, and Quantum Cybersecurity. As a part of a team, I was one of the main contributors developing the presentation of a race car game we developed and tested in Python, along with its transcript. You can view my specific section on slide 8 regarding the testing of the application throughout the SDLC by clicking 'LEARN MORE' below. In fact, I not only contributed to add most of the unit tests to the codes developed, but also automated both unit test checks in a CI/CD pipeline via GitHub actions, which also runs automated checks for both security vulnerabilities and code quality/linting, thus not only verifying the correctness of the main applications’ functionalities, but also its security. Furthermore, I had created a conda virtual environment for us to work on the same environment in Python with the same dependencies, and created a Python package out of our application that can be installed via pip. I was also the main reviewer of my team members’ pull requests, aiming to achieve a high-quality software and presentation as per the assignment’s requirements.
Unit 12: Future Direction of Software Engineering Project Management
In this unit, I had the chance to analyse the future direction of software engineering project management and ponder on our team's work and my contributions to it. In this software engineering project, I leveraged security by design as a key principle and automated security checks for any vulnerabilities in the open-source libraries used in Python via two libraries, 'safety' and 'bandit'. I automated these checks by including them in the CI/CD pipeline I built via GitHub Actions, which can be viewed by clicking 'LEARN MORE' below.
Reflections on Software Engineering Project Management
In this reflective piece, I summarised what I learnt on Software Engineering Project Management in this module, the work I carried out throughout it, and their impact on my personal and professional development. Click ‘LEARN MORE’ below to read it.