Quality starts with the process: Addressing common gaps in software testing

There is no industry without a need for quality software. Recently, automaker Ford recalled more than 355,000 trucks due to a problem with the instrument panel display; an error that risks hiding sensitive information such as speed and, subsequently, increasing the likelihood of car crashes. While not all software failures have dramatic consequences, many organizations feel the pinch of poor quality. In fact, more than two-thirds (66%) say they are at risk of software outages within a year, with 40% of technology leaders and professionals saying downtime costs them more than $1 million a year.

Too hasty or poorly tested releases can lead to increased failures – as seen at Ford – leading to costly downtime and user frustration. Software quality often slips not because of big bugs, but because of small cracks in the software development life cycle (SDLC). Weak feedback loops, unclear metrics, and manual constraints can cause lasting damage.

Nearly one-third of software development teams say poor developer quality assurance (QA) communication is a major obstacle to their software quality, while more than a quarter (29%) cite a lack of clear quality metrics. If not addressed, these challenges introduce themselves into organizations, destroying software quality at its core. Software failures are caused not just by code, but by culture, which is why strong, shared testing processes are essential to keep them strong.

Root failure in software testing procedures

Unfortunately, communication breakdowns between developers and QA teams are common, and when feedback does come, it’s often inconsistent or unclear. These weak feedback loops can lead to long specification cycles, or worse, fragmented testing efforts with duplicative work and rework. While all of this can delay problem detection, broken feedback loops are part of the problem.

Often times, different stakeholders define quality in conflicting ways. It is common for small technical stakeholders to focus on metrics that emphasize speed, for example, while development teams may choose to focus on key quality indicators such as defect rates and user experience to judge their success. Without agreeing on business-wide quality metrics, teams lack clear direction on how to best manage their time and resources. Such a lack of understanding makes it difficult to allocate research resources effectively and focus on the most important areas of the business.

Once teams are guided on what to measure, execution can often falter. Relying on manual, ad hoc testing creates friction across teams and makes it nearly impossible to measure effectively. Without standard or automated procedures, results vary from one cycle to another, reducing delivery and increasing the risk of missed injuries. Over time, this lack of structure prevents organizations from achieving the speed, efficiency, and reliability required for modern software development.

Creating a powerful evaluation process

To set organizations up for success, software quality must be considered a collaborative effort, not left to one team or one development phase. Starting with a shared responsibility model makes each team accountable for quality at each stage of the SDLC, from design to delivery. This requires clearly defining team roles, setting distinct goals, and ensuring that all teams participate fully in the review and planning process.

This shared ownership can be strengthened by establishing a common language for measuring performance. Developing a concise set of key performance indicators (KPIs) can help identify wins and highlight areas for improvement. Coupling this with cross-functional reviews, which involve internal teams and even customers, can help identify problems earlier. With timely responses, context is saved for developers, quick fixes and preventing small problems from snowballing. Formalizing these processes allows feedback to become part of the workflow itself, reinforces accountability and helps teams build empathy for each other’s challenges.

Importantly, KPIs must go beyond output-focused measures such as speed of release to include outcomes that align with user experience and business objectives. When used consistently, integrated metrics can help guide insight-driven decisions and turn quality into a strategic lever.

Strengthening and scaling

Once these basic processes are in place, organizations can take the next step by layering on automation and advanced tools. These skills strengthen process discipline, reduce diversity, and strengthen consensus across teams. Among the most influential tools is AI, which can measure quality processes beyond what manual methods can achieve, helping software development teams move faster without sacrificing reliability. It can act as an accelerator and help maintain high standards as systems grow in complexity.

However, the real benefits of AI will only be realized if the process gaps are addressed first. Without a strong structure, the risk of automation increases existing inefficiencies and increasing technical debt. By addressing these critical issues early, businesses can ensure that AI becomes the next driver of intelligent, robust delivery for years to come.