| Code Quality (CQ) is an ideal. Your feeling about the magnitude of the issue (most companies) is wrong. Programmers discuss CQ principles among themselves. However, the discussion becomes more challenging with management. Management is responsible for accomplishing business objectives.
Development and testing timeframes are at odds with business objectives. Adopting CQ delays product. If you're going to delay product, but the product will be beautifully efficient, idiomatic, elegant and possibly a little faster than the first pre-CQ version, you're not going to win an argument in an organizational context where delivery timelines matter. Time and effort are not a programmer's friend in a task-driven organizational setting. Fortunately, real-time linters tell programmers not only about material errors but present stylistic warnings (such as Python pep8 linters). Further, static analysis tools such as Quantified Code [1] conduct an in-depth analysis of code and suggest stylistic improvements. I suspect that this is an area where machine learning will advance Code Quality further. Maybe, just as there are language servers, there will be code quality servers. It is worth noting that the QuantifiedCode entity shuttered in the Summer of 2017. It's not clear why the company closed-- did they fail to monetize automated code review? Were they acquired? In conclusion, the more you can automate code quality-related improvements, the more likely you can promote your Code Quality ideals. [1] https://github.com/quantifiedcode/quantifiedcode |