University of Technology Sydney

41129 Software Innovation Studio

Warning: The information on this page is indicative. The subject outline for a particular session, location and mode of offering is the authoritative source of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.

Subject handbook information prior to 2024 is available in the Archives.

UTS: Engineering: Computer Science
Credit points: 12 cp

Subject level:

Undergraduate

Result type: Grade and marks

Requisite(s): (31251 Data Structures and Algorithms AND (41113 Software Development Studio OR 41127 Software Design Studio OR 41093 Software Engineering Studio 1A))
Anti-requisite(s): 41097 Software Engineering Studio 3A

Description

Innovation typically leads to ideas and concepts regarding products or product features. Meanwhile, the data from end users have become very important for better software. A growing number of software engineers are using state-of-the-art algorithms to streamline the software and provide better services. Software innovation studio focuses on field research, observation of users’ needs, algorithm implementation, innovative design, mutual communication, teamwork, critical solutions and software testing. Students learn innovative idea generation skills, logical thinking, systematic implementation, and agile management.

The subject allows students to experience the whole software engineering cycle and accomplish various experience from a practice- and project-based learning environment. The innovative outcome can be a software/system/solution, new features/functions for existing software/platform, and/or resolve complex software issues, to create significant value and impacts to end-users as well as the society.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:

1. Develop and improve innovative software that fits users’ needs. (C.1)
2. Apply systematic and logical data analytics for software engineering. (D.1)
3. Reflect on skills learned as the final step in the development of one’s career plan. (F.1)
4. Apply agile and systematic approaches to manage software engineering projects, as an efficient team. (E.1)

Course intended learning outcomes (CILOs)

This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):

  • Design Oriented: FEIT graduates apply problem solving, design and decision-making methodologies to develop components, systems and processes to meet specified requirements. (C.1)
  • Technically Proficient: FEIT graduates apply abstraction, mathematics and discipline fundamentals, software, tools and techniques to evaluate, implement and operate systems. (D.1)
  • Collaborative and Communicative: FEIT graduates work as an effective member or leader of diverse teams, communicating effectively and operating within cross-disciplinary and cross-cultural contexts in the workplace. (E.1)
  • Reflective: FEIT graduates critically self-review their performance to improve themselves, their teams, and the broader community and society. (F.1)

Contribution to the development of graduate attributes

Engineers Australia Stage 1 Competencies

This subject contributes to the development of the following Engineers Australia Stage 1 Competencies:

  • 1.3. In-depth understanding of specialist bodies of knowledge within the engineering discipline.
  • 2.3. Application of systematic engineering synthesis and design processes.
  • 2.4. Application of systematic approaches to the conduct and management of engineering projects.
  • 3.2. Effective oral and written communication in professional and lay domains.
  • 3.3. Creative, innovative and pro-active demeanour.
  • 3.5. Orderly management of self, and professional conduct.
  • 3.6. Effective team membership and team leadership.

Teaching and learning strategies

This studio-based subject provides a learning environment to leverage big data analytics for innovative software development. Students will practice the implementation of big data analytics and logical thinking. The students will also form small self-managed teams to practice agile project planning for a proof of concept, prototype, problem solve and/or minimum viable product with innovative ideas or concepts. Teams are aided and guided by mentors (tutors, industry partners and academic researchers). To encourage high technical standards and peer learning, all teams develop the software based on specified requirements but are free to decide how those requirements can be implemented to achieve greater satisfaction(s) or fit users’ need(s). The team must have at least one discussion every week to review the project scope, progress, achievements and risks/hazards. Students are also expected to demonstrate their collaboration as well as communication skills during the teamwork and weekly discussions. Students will participate in peer evaluations of teamwork by SparkPLUS as well.

Students are encouraged to use the outside of workshop time to read latest articles and work with their team members. Lecturers, tutors and mentors will provide feedback regarding student achievements, results of meeting, logical thinking, teamwork and hazards in the workshops.

Formal assessments of the innovative concept, project achievements and deliverables occur at the early, middle and end of the semester. The lecturers, mentors, tutors and peers will all involve in the formal assessment and provide feedback through online tools and workshops.

Content (topics)

In this subject, students will experience the following:

  1. Develop innovative software for users’ needs
  2. Data analytics
  3. Logical Thinking
  4. Configuration management including source and version control
  5. Agile software management
  6. Team communication and collaboration
  7. Reflective software design practice
  8. Software development and unit testing

Assessment

Assessment task 1: Project Pitch

Intent:

The purpose of this task is how well the students are learning to

  1. deliver innovative idea(s)
  2. do comprehensive field research and background analysis
  3. do proper project planning
Objective(s):

This assessment task addresses the following subject learning objectives (SLOs):

1, 2 and 4

This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):

C.1, D.1 and E.1

Type: Presentation
Groupwork: Group, group assessed
Weight: 10%
Length:

10 – 15 minutes presentation

Assessment task 2: Implementation and logical thinking

Intent:

The purpose of this task is to

  1. reflect their work progress and plan weekly,
  2. design system/software,
  3. leverage data analytics,
  4. make proper decisions
Objective(s):

This assessment task addresses the following subject learning objectives (SLOs):

1 and 2

This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):

C.1 and D.1

Type: Report
Groupwork: Individual
Weight: 35%
Length:

1000 – 3500 words

Assessment task 3: Engagement and teamwork assessment

Intent:

The purpose of this task is to demonstrate and understand how well the communication and collaboration in the team.

Objective(s):

This assessment task addresses the following subject learning objectives (SLOs):

3 and 4

This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):

E.1 and F.1

Type: Reflection
Groupwork: Group, individually assessed
Weight: 10%
Length:

500 – 2000 words

Assessment task 4: Product assessment

Intent:

The purpose of this task is to learn how to deliver a software/system against the users’ needs.

Objective(s):

This assessment task addresses the following subject learning objectives (SLOs):

1, 2, 3 and 4

This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):

C.1, D.1, E.1 and F.1

Type: Demonstration
Groupwork: Group, individually assessed
Weight: 35%
Length:

15-20 minutes demonstration and presentation

Assessment task 5: Technical reading and algorithm implementation

Intent:

The purpose of this task is to gain the knowledge as well as programming experience for big data analytics

Objective(s):

This assessment task addresses the following subject learning objectives (SLOs):

1 and 2

This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):

C.1 and D.1

Type: Quiz/test
Groupwork: Individual
Weight: 10%
Length:

5 quizzes in the whole semester

The answers to the questions in every quiz will come from:

1 hour article reading (before workshop) + 1 hour hand on tutorial (during workshop)

Minimum requirements

In order to pass the subject, a student must achieve an overall mark of 50% or more.

Recommended texts

  • Bernard Marr (2018), Data Strategy: How to Profit from a World of Big Data, Analytics and the Internet of Things
  • Hans Weber (2020), Big Data and Artificial Intelligence: Complete Guide to Data Science, AI, Big Data and Machine Learning
  • Beck, K. (2003), Test-driven development: By example, Addison-Wesley, Boston
  • Robert C. Martin (2008), Clean Code: A Handbook of Agile Software Craftsmanship
  • Sommerville, I. (2011), Software Engineering: Ninth Edition, Addison-Wesley
  • Ambler, S. W. and Lines, M. (2012), Disciplined Agile Delivery, IBM Press