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Jet Hughes
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---
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title: "11-continuous-integration-2"
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title: "11-continuous-integration-2"]
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sr-due: 2022-04-07
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sr-interval: 3
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sr-ease: 250
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tags:
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- cosc202
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- lecture
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@ -20,11 +23,11 @@ CI usually tets abd buiolds your prokects
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runs on a repo server. Usuially persistent, internet accessible
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## 1 Gitlab overall architecture
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## 0.1 Gitlab overall architecture
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 : not in exam
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- many different services used
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## 2 Gitlab runners
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## 0.2 Gitlab runners
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run CI scripts
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- gitlab.com is a cloud computing service
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@ -36,7 +39,7 @@ run CI scripts
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- e.g., to use a particular GPU, or other hardware you have
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- GItlab runner itself is a small program written in Go
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### 2.1 Runner architecture
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### 0.2.1 Runner architecture
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- runs jobs
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- on isolated infrastructure
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@ -51,7 +54,7 @@ RHS shows GitLab.com's CI hosting: uses google cloud
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 : not in exam
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## 3 How CI chagned website hosting
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## 0.3 How CI chagned website hosting
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- need to share stifacts produced by CI jobs
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- using the web to share artefacts is ideal
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@ -64,7 +67,7 @@ RHS shows GitLab.com's CI hosting: uses google cloud
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e.g., https://cosc202.cspages.otago.ac.nz
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## 4 Debugging CI scripts
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## 0.4 Debugging CI scripts
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- first ensure config files YAML is valid
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- vuilt in gitlab editor checks YAML as you type
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@ -76,7 +79,7 @@ e.g., https://cosc202.cspages.otago.ac.nz
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- e.g., `if command supposed to fail; then true; else true; fi`
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- Complex scripting? Beste to put script in a file and run it from CI
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## 5 Secrets used by CI scripts
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## 0.5 Secrets used by CI scripts
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@ -1,7 +1,154 @@
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---
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title: "evaluating-designs"
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sr-due: 2022-04-07
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sr-interval: 10
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sr-ease: 210
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tags:
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- info203
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---
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#unfinished
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Why to evaluate using 'outside' people:
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- how do we know if a [[Prototyping|prototype]] is good
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- designer/developers are not 'fresh' -> they already have experience with the product
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- designer/developers don't know what real users will do
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## 0.1 Issues to consider
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- Reliability/precision
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- how accurate is your study?
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- Is is reproducible -> if it was repeated, would you get the same result
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- Generalizability
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- Is your sample representative
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- Realism
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- Would observed behaviour also occur in the wild
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- Comparison
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- Shows how different options were recieved
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- rather than a "people liked it" study
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- work involved/efficiency
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- How cost efficient are your methods
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## 0.2 Factors to consider when choosing an evaluation method
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- Stage in the cycle at which the evaluation is carried out -> (design / implementation)
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- Style of evaluation -> (lab / field)
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- Level of subjectivity or objectivity
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- Type of measurement -> (qualitative / quantitative)
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- Information provided -> (high-level / low-level)
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- Immediacy of response -> (real-time / recollection of events)
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- Level of interference implied -> (intrusiveness)
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- Resources required -> (equipment, time, money, subjects, expertise, context)
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## 0.3 Styles of evaluation
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##### 0.3.1.1.1 Laboratory Studies
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- 1st step: Designer evaluates his/her UI
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- Specialised equipment for testing available
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- Undisturbed (can be a good or bad thing)
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- Allows for well controlled experiments
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- Substitute for dangerous or remote real-world locations
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- Variations in manipulations possible / alternatives
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##### 0.3.1.1.2 Field Studies
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- Within the actual user’s working environment
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- Observe the system in action
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- Disturbance / interruptions (+/-)
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- Long-term studies possible
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- Bias: presence of observer and equipment
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- Needs support / disturbs real workflow
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## 0.4 Quantitative vs Qualitative methods
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##### 0.4.1.1.1 Quantitative Measures
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- Usually numeric
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- E.g. # of errors, time to complete a certain task, questionnaire with scales
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- Can be (easily) analysed using statistical techniques
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- Rather objective
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- Most useful in comparing alternative designs
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- Test hypotheses
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- Confirm designs
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##### 0.4.1.1.2 Qualitative Measures
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- Non-numeric
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- E.g. survey, interview, informal observation, heuristic evaluation
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- Difficult to analyse, demands interpretation
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- Rather subjective
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- User’s overall reaction and understanding of design
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- Generate hypotheses
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- Find flaws
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## 0.5 Stage in cycle
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##### 0.5.1.1.1 Design Stage
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- Only concept (even if very detailed) exists
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- More experts, less users involved
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- Greatest pay-off: early error detection saves a lot of development money
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- Rather qualitative measures (exceptions: detail alternatives; fundamental questions, ...)
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##### 0.5.1.1.2 Implementation
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- Artefact exists, sth. concrete to be tested
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- More users, less experts involved
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- Assures quality of product before or after deployment; bug detection
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- Rather quantitative measures (exceptions: overall satisfaction, appeal, ...)
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## 0.6 Methods
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### 0.6.1 Usability studies
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- Bringing people in to test Product
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- Usage setting is not ecologically valid - usage in real world can be different
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- can have tester bias - testers are not the same as real users
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- cant compare interfaces
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- requires physical contact
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### 0.6.2 Surveys and focus groups
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+ quicly get feedback from large number of responses
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+ auto tally ressults
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+ easy to compare different products
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- responder bias
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- Not accurate representation of real product
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* e.g., ![[Pasted image 20220316130318.png]]
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* Focus groups
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* gathering groups of people to discuss an interface
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* group setting can help or hinder
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### 0.6.3 Feedback from experts
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- [[Peer critique]]
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- [[Dogfooding]]
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- Using tools yourself
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- [[Heuristic Evaluation]]
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- structured feedback
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### 0.6.4 Comparative experiments
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- in lab, field, online
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- short or long duration
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- which option is better?
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- what matters most?
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- can see real usage
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- more actionable
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### 0.6.5 Participant observation
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- observe what people do in the actual evironment
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- usually more long term
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- find things not present in short term studies
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- [[Observation]]
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### 0.6.6 Simulation and formal models
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- more mathmatical quantitative
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- useful if you have a theory to test
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- often used for input techniques
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- can test multiple alternatives quickly
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- typically simulation is used in conjugtion with [[monte carlo optimisation]]
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## 0.7 Query techniques
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- [[Interviews]]
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- questionnaires
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- less flexible
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- larger samples possible
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- design of questionnaire is for expert only
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- use of standard (proven) questionnaires recommended
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- types of questions:
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- general (age, gender)
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- open ended
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- scalar (e.g., likert-like scales)
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- multiple choice
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- ranking
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## 0.8 Users
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- users can come up with great ideas
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- lead user -> need specific soluton that does not exist -> often make up their own solution
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- extreme user -> use existing solution for it's intended purpose to an extreme degree
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- typical user ->
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