How can you balance speed and quality when delivering product updates?

Quality standards in product development encompass functionality, performance, reliability, usability, security, and scalability. They ensure the product meets its intended purpose accurately, operates efficiently, and provides a positive user experience while safeguarding against breaches and accommodating future growth. Balancing speed and quality involves efficient workflows, prioritizing customer needs, automated testing, and agile methodologies to deliver timely updates without sacrificing quality.

One of the most incredible benefits of any agile methodology is the opportunity to develop in tandem with parallel and adjacent teams. For example, providing developers with real-time QA feedback can shorten production times, lessen the number of high-priority bugs, and quicken a company's time-to-market; production times can decrease by up to 40% when adequately implementing agile methods. However, it's vital to remember that there's no one-size-fits-all approach in agile, and it's essential for companies to frequently assess their processes and determine the best agile method to suit their current and future needs.

While using agile methods it's important not to overcommit. I always prefer under committing, then over delivering. For example, if the team works in Scrum & they follow a 2 week sprint structure, it's important to look at past data & learn to estimate the stories more accurately. Once the team understands their normal velocity, it will be easier to decide how many story points to commit to a sprint in order to complete the sprint successfully (which means the work is delivered to production to highest standard). This means the team is not compromising on speed or quality.

Si on souhaite allier vitesse et qualité, il faut faire des acteurs de la qualité les parties prenantes des opérations. J’entends par là, qu’il est important de tenir compte du temps que représente un travail de qualité : temps d’établissements des livrables, auto-contr?le, vérification avant diffusion de ceux-ci. Si on chiffre au plus juste alors la vitesse de réalisation sera en adéquation avec la qualité finale souhaitée ! Trop souvent on rogne sur des actions qualitatives pour améliorer les marges du chiffrages, ce qui conduit à de la non-qualité…

While automated tests can be a handy tool for many companies, we must remember that automation is best after structuring an established test plan and workflow. Like manual tests, automation can be time-consuming, inaccurate, and ineffective if implemented before a company has the infrastructure to support them. They can be costly to maintain, and since most small to medium-sized companies see frequent code changes, keeping those tests up-to-date can require significant time and effort. Most companies remain in a constant maintenance loop instead of developing and releasing new features. However, once a company has established a robust test workflow, automating regression and unit tests can significantly reduce the time between releases.

Automating test cases can largely contribute towards the efficiency of a deliverable. Dividing automation between functional and regression test cases effectively help towards in sprint vs end to end testing.

All this is true but I can recommend automating testing only those parts of your product that work truly reliably. Otherwise, we need to update autotests for every change in this module becouse they will not work.

Data about your data contributes to a deliverable after it has been delivered. It’s a great litmus test to see how well a product is working vs how well does a certain feature sits with the audience. This information can generally help in making decisions around future developments and deciding what feature should be continued to be built and what features should be dropped