along with best practices Intro & Definition Software quality “the degree to which software meets specified requirements and user expectations” Measurement “the process of quantitatively assessing specific attributes or characteristics of software using defined metrics or criteria.” Data-driven decision-making “the process of using data to inform your decision-making process and validate a course of action before committing to it.” Intuition “the ability to understand something instinctively, without the need for conscious reasoning.”
Identify defects early in the development process Ensure compliance with standards Enhance customer satisfaction ROI % = Cost of Late Fix - Cost of Early Detection Cost of Early Detection X 100 Looks easy, not passing regulation = no product = 0% revenue After the regulation is passed extra quality consideration uses the same ROI as above Same as the first for preventive quality It’s own complex formula for reactive quality
simplest one ROI % = Cost of Late Fix - Cost of Early Detection Cost of Early Detection X 100 Cost of late fix (simplified example): ⬢ Average cost estimation of fix after deployment ⬢ Average cost estimation of fix during development ⬢ Estimation of the cost of an issue hitting production on the product revenue ⬢ Estimation of the cost of downtime ⬢ Sum of all cost - cost of early fix What period of time is relevant, do you just guess? Does your team input the time, do you just guess? Is your revenue, marketing or customer support team capable of estimating that impact, where is that data coming from? Do you have actual uptime statistics to infer this value? If some of those info is missing, should you invest in acquiring them? What is the ROI for it?
to innovation” A wise man, maybe Some examples ⬢ Changing behaviour ⬢ Removing biases (especially your own) ⬢ Negotiating budgets ⬢ Backing evaluation arguments ⬢ Support fact based decision making ⬢ Getting yourself promoted (you never know!) ⬢ Anything you need in your specific situation
Rate Code Complexity Test Coverage Mean Time Between Failures Identify most complex codebase Eventually reducing bugs escapes Under report bugs No - Unit of software size is not easy and likely inconsistent across teams Identify most complex codebase Eventually reducing bugs escapes Under report bugs No - Unit of software size is not easy and likely inconsistent across teams Identify most complex codebase Eventually reducing maintenance costs Not really, easy to ignore though Yes - all languages can report basic metrics with ease Identify less secure pipelines Eventually reducing bugs escapes Yes especially for unit tests Functional tests highly depending on expertise Yes - unit test coverage is built in most IDE No - functional coverage is complex Identify product with the lower quality Eventually increasing stability Under report bugs Under report failure duration No - depending on human input
are useless” Cost “This is too hard”, “we can’t prioritize reporting” Accuracy “The data is inaccurate, leading to incorrect decisions” Personal feelings “Those report will be used against me, and they are not representative of the value I provide here” “This will motivate others to only focus on meeting that specific metric and not try to do their best” “Why are you measuring this, you do not trust me?”
Change management ⬢ Service up time ⬢ Customer raised tickets ⬢ Company revenue ⬢ Communication ⬡ Transparency ⬡ Trust ⬡ Honesty ⬢ Measure teams or dept ⬢ Politics
Do you need metrics? Design your metric Build data pipeline Communicate Profit Concern about our slow response time, especially for first investigation and estimates The solution should be team specific, and thus driving the solution through team result Establish SLO per severity, report the % of tickets above SLO per severity Record time of creation, time of first review by the dev team, add all filter necessary Explain the problem, the aim, the metric and the change in process to record the data Use it to measure the result of team practice changes, and discuss other ideas.
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