Back to: Software Development (VCE Units 3 & 4)
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For the purposes of this study design and associated assessment, the following definitions will apply.
| Term | Definition |
|---|---|
| Alpha testing | A testing phase that checks whether modules or solutions meet all requirements and function as expected. Alpha testing is carried out by developers, independent testers or high-level users in a development or testing environment throughout the development phase. |
| Archiving | The process of moving data from a system that no longer needs to be accessed regularly to a less frequently accessed storage area for future use or to meet compliance requirements. This ensures that data can be stored separately to systems for long periods of time without impacting on current performance or storage requirements. |
| Backup | The process of making a copy of data and storing the copy separately to the original data in case it is needed due to data loss. Backed up data can be full (entire copy of data), differential (changes since last full backup), incremental (changes since last backup) or a combination of these. Backups can either run manually or be scheduled to run automatically, and can be stored on a local hard drive (distinct from the original source), on external storage devices or by using cloud computing. Backups are restored when data loss occurs. |
| Beta testing | A testing phase that checks whether solutions meet all requirements, function as expected, are stable and are usable by intended user groups. Beta testing is performed in a development, testing or production environment with hardware identical (or similar) to that on which it will be implemented on by users of the solution. Feedback from beta testing can be used to recommend or make modifications to modules or solutions. |
| Computational thinking | The process of recognising aspects of computation and being able to think logically, algorithmically, recursively and inferentially. It typically involves the components of decomposition, pattern recognition, abstraction, modelling and algorithms, which may be used to create digital solutions. |
| Control structures | Code structures that define the algorithmic behaviour of a software solution. For the purposes of this study design: * Sequence: All lines of code are executed in the same order they appear. * Selection (or branching): Code is executed based on the evaluation of a condition and the data provided. Examples include IF, IF…ELSE, IF…ELSEIF and SWITCH/CASE. * Repetition (or iteration): Code is executed repeatedly based on the evaluation of a condition. Examples include pre-test (WHILE loops), post-test (DO…WHILE/REPEAT…UNTIL loops) and repeating a block of code a set number of times based on context and the data provided (FOR loop). |
| Conventions | General or commonly accepted ways of working with digital systems or representing data to ensure consistency across a solution. Examples include (but are not limited to): * how data might be aligned within the cells of a spreadsheet * using a consistent axis scale in a chart. Conventions can be enforced, such as when passwords must have specific characters, or recommended, such as including a subject line in emails. |
| Critical and creative thinking | The process of using a range of techniques when developing ideas and designs. It involves composing, analysing and evaluating questions, data and decisions. Students explore the use of these strategies when managing the process of solving problems. |
| Cyber security | A multi-faceted field incorporating aspects of digital systems, organisational practices, threats to data and systems, law, ethics and risk management. Cyber security measures focus on protecting systems and data from a range of threats. |
| Data analytics | The processes and tools that allow organisations to acquire and/or extract data in various forms, update erroneous or incomplete data, analyse the data to identify trends, relationships and patterns, draw inferences about the data and present findings using visual methods that provide clear and unambiguous conclusions. |
| Data integrity | A method of describing the overall accuracy, authenticity, correctness, reasonableness, relevance and timeliness of data. Data with high integrity is more reliable and trustworthy, while data without integrity should not be used in calculations or be trusted to make decisions. |
| Data sources | Data accessed by or provided to a solution by an external file or system. Sources may be unstructured (plain text files) or structured (delimited files or XML formats). See also Primary data and Secondary data. |
| Data visualisations | The process of using software tools to present analysed data, such as a graphic representation, usually by combining charts, histograms, graphs, maps and network diagrams, in a visually attractive and informative way. Data visualisations help users explore data to identify patterns and relationships in large amounts of data. Data visualisation tools allow graphic representations to be static or dynamic and can incorporate virtual reality and augmented reality. |
| Debugging | A process used when developing solutions using a programming language that involves identifying existing errors, fixing the errors and testing to see if the changes made are correct. Debugging occurs during the development of a solution as code is written. |
| Descriptive statistics | A collection of measures that can be used to provide a summary of a data set. These can be referred to as measures of central tendency (average, median) or measures of variability and spread (maximum, minimum, range, frequency, standard deviation). |
Design principles | Accepted characteristics that contribute to the functionality, usability and appearance of solutions. In this study, the principles are related to: * functionality (interactivity and navigation) * usability (ease of use and accessibility) * appearance (alignment, balance, contrast, image use, space, text and table formatting). |
| Design thinking | A way of thinking critically and creatively to generate and evaluate innovative ideas, and precisely define the preferred solution so it can be created using a digital system. It involves an understanding of the needs of users and of ways of creating solutions that are more efficient or effective than existing ones. When designing, students use both divergent and convergent thinking skills. Divergent thinking supports creativity and the generation of a range of ideas. Convergent thinking supports the selection of a preferred solution and the preparation of accurate and logical plans and instructions to digitally create the solution. |
| Digital system | Refers to elements such as hardware and software, and their interconnectedness, used to create digital solutions. When digital systems are connected, they form a network. |
| Dynamic data visualisations | Graphical representations of complex data or information. They allow the exploration of data in an interactive way. In contrast to a static visualisation or chart, a dynamic data visualisation contains data that can change in response to user interaction or the addition of live data. |
| Effectiveness | A measure of how well solutions, designs, data and information security strategies and development practices function, and the degree to which they achieve their intended purpose. Effectiveness measures for solutions and designs include accessibility, accuracy, attractiveness, clarity, communication of message, completeness, maintainability, readability, relevance, timeliness and usability. Effectiveness measures for data and information security strategies include confidentiality, integrity and availability. Effectiveness measures for development practices include security controls in place, exposure to vulnerability and risk, and legal compliance. |
| Efficiency | A measure of how much time, cost and effort is applied to achieve intended results. Measures of efficiency in a solution could include the cost of data and file manipulation, its functionality and the speed of processing. Measures of efficiency in a network include its productivity, processing time, operational costs and level of automation. |
| Encryption | The process of encoding data and information from a plain text format. This is done to protect data and information from being compromised. There are two methods of encryption: symmetric and asymmetric. Symmetric encryption involves the use of a single key for the encryption and decryption of data. Asymmetric encryption involves the use of a public key for the encryption of data and information and a private key for the decryption of the data and information. |
| Errors (programming) | Issues that arise that prevent a software solution from functioning as expected. Within software development, errors can be categorised as: * syntax errors: errors in code that prevent a solution from being compiled and executed as a result of a programming language’s syntax not being followed correctly * logic errors: errors in code that result in the solution generating incorrect output, such as calculations or evaluating conditions * run-time errors: errors that arise during program execution that result in the solution crashing if not handled correctly. Examples include overflow, index out of range, type mismatch and divide by zero errors. |
| Ethics | Issues that arise that challenge moral standards, principles or expectations and that can impact individuals and/or organisations. |
| Format | The physical appearance in which data and information can be presented. These include images, graphs, tables, text and web pages. Formats specify characteristics such as presentation style or arrangement, shape and size. |
| Ideation | A process for generating and developing ideas that follows a cycle from starting with a concept through to developing a design. Ideas can be expressed as text, images and drawings, and in verbal form. Ideation tools can include mood boards, brainstorming, mind maps, sketches and annotations. |
| Identity and access management | The process of ensuring that the members of an organisation can access only the data, modules and systems required to perform their designated duties. When implemented effectively, this ensures that: * individuals cannot access data, modules or systems beyond their needs * staff are not granted administrator or high-level privileges (unless necessary) * there is a decreased exposure to risk and security breaches for the organisation. |
| Industry frameworks | Guidelines developed to ensure organisations and governments follow strategies for protecting the security of their data and information with networks and maintain the highest ethical standards. For the purposes of this study, the following frameworks are relevant: * Australia’s Artificial Intelligence (AI) Ethics Principles * Essential Eight * Information Security Manual (ISM). |
| Infographics | Graphical representations of complex data or information that provide an overview of a topic or area of interest. They rely on combining visual elements to communicate data patterns or trends quickly and clearly or information as data visualisations. These include complementary colour schemes, easy-to-read text fonts, headings, multiple graphs, simple charts and statistics. |
| Legal requirements | Key legislation (acts) that individuals, organisations and governments are expected to comply with in relation to intellectual property and the privacy of data and information. For the purposes of this study, the following acts are relevant: * Copyright Act 1968 (Cwlth) * Health Records Act 2001 (Vic) * Privacy Act 1988 (Cwlth), including Privacy Amendment (Enhancing Privacy Protection) Act 2012 and Privacy Amendment (Notifiable Data Breaches) Act 2017 * Privacy and Data Protection Act 2014 (Vic). |
| Naming conventions | A set of guidelines for providing consistency in the naming of entities during the development of digital solutions. These include program names, function names, interface controls, variable names, table names and file names. Naming conventions can minimise the effort needed to read and understand the solution or code. |
| Primary data | Data that is collected by researchers directly from sources using methods such as surveys, interviews and observations. The data is raw and has not been processed or summarised. |
| Project management | Detailed proposal for managing projects considering requirements and constraints. Features of project management include identification of tasks, sequencing, time allocation, dependencies, milestones and critical path. |
| Pseudocode | A series of English-like statements used to represent an algorithm that will solve a problem. Though there is no standard to pseudocode, there are a number of conventions, including: the use of START/BEGIN and FINISH/END to indicate the beginning and end of a program IF…ELSE and SELECT to represent decisions/selections REPEAT…UNTIL, WHILE…DO and FOR…NEXT to represent loops/iteration/repetition. |
| Secondary data | Data that has been previously collected, summarised or analysed by someone other than the researcher. These can be large amounts of data and stored in repositories or data sets, and could also include reports and articles. |
| Security controls | Hardware, software, physical equipment, procedures and electronic measures used to assist in the protection of individuals, systems, networks, organisations and data that is collected, communicated and stored. |
| Security threats | The actions, devices and events that threaten the integrity and security of data and information stored within, and communicated between, digital systems. The threats can be: * accidental: deletion or overwriting of data, misdelivery of information and unintended equipment damage * deliberate: insider threats, unauthorised access, theft of data or physical devices, malware, denial of service attacks and social engineering * events-based: natural disasters and environmental factors, power or network outages, hardware failures and data corruption. |
| Software requirements specification (SRS) | The intended purpose and environment of a software solution. It documents the key activities associated with the analysis stage of the problem-solving methodology. Features of an SRS should include a description of the functional and non-functional requirements, constraints, scope, user characteristics and technical environments. |
| Systems thinking | A holistic approach to the identification and solving of problems. Systems thinking involves analysing the interactions and interrelationships between components of individual information systems (data, processes, people and digital systems) to identify how they are influencing the functioning of the whole system. |
| User experience (UX) | The consideration of how users perceive and respond when interacting with a digital system. Characteristics of UX include affordance, interoperability, security (authentication and data protection) and usability. |
| Validation | The checking of data for its reasonableness and completeness by manual or electronic means. Validation of data includes: * existence or presence checking, which verifies that a required field has a value entered and is not empty or blank * type checking, which confirms that data entered is of a particular type * range checking, which involves ensuring that data entered falls within a certain range. Validation may occur as the data is input or once it has been stored. |
| Verification | The checking of data for accuracy and consistency that occurs after data acquisition/entry and throughout the process of manipulation and analysis. This can include proofreading, confirming the data against other sources and checking that the data visualised is consistent with the data acquired/collected. |