The 9 Risky Shots For Quality Engineering

QE Unit
6 min readMay 28, 2021

Quality Engineering will survive the various challenges encountered in transverse in the organization?

This is a good question for an emerging practice, at the crossroads of several disciplines, between Business and IT.

You can read an introduction to the Quality Engineering perspective in this article.

In order to answer it, I have selected 9 challenges that Quality Engineering must face to maintain its contribution of value in the ecosystem.

#1 Address the (real) issues

Let’s put ourselves in a situation.

As a good Quality Engineer, you are connected to the customer complaint system, not just to IT ticketing.

Several customers complain about a degraded purchase and post-purchase experience, raising cross-functional issues in the organization.

Where to start? What data to analyze, correlate, highlight? What problems seem to be underlying?

The increasing complexity of ecosystems and organizations makes it difficult to navigate.

Problem management is a demanding practice, especially if applied to the whole organization.

Identifying, focusing and solving the real issues is a real challenge, exemplified by the amounts of projects not delivering the expected value.

The answers are not in the modalities of execution, in a multi-year plan or in agile sprints, in SOA, microservices, but in the subjects addressed.

Problem management is an organizational skill that Quality Engineering must animate, to identify real problems and improve the structural performance of the system.

Counter intuitively, problem-solving fundamentally needs human interactions.

#2 Develop a collaborative organizational system

Even if uncertain and unpredictable, the structure and organization of a system allow behavior to be supervised.

Regardless of the model, the system must create a capacity for collaboration outside organizational and hierarchical silos, supporting problem-solving.

The organization must therefore promote transversal exchanges when they are necessary while orchestrating manual, semi-automatic and automatic tasks between the actors.

Missions, values ​​and other incentive mechanisms must be reviewed to align with the target of organizational changes.

The various elements of the system must be considered and approached as a corporate change management project as such.

In addition, performance measurement must evolve to promote the development of the system.

#3 Driving a Purpose-Driven and Value-Driven organization

The energy, vision and culture through which employees are driven is the fuel for change.

Asking the following question is often rich in enrichment: “What are you contributing to?”

A response relating to the next development tickets in progress denotes a lack of sharing of a common vision and culture.

On the other hand, you can hear “I am participating in the construction of our platform of tomorrow at the service of our customers”.

The shared vision that drives employees is one of the key characteristics of a Purpose-Driven organization.

On this subject, I personally recommend the book Man’s Search for Meaning.

Notable advantages emerge such as better initiative, motivation at work and collaboration in the service of company performance.

The advantages of a Value-Driven organization are explicit in its definition.

A team is value-driven when the team members value working together; they are constantly improving themselves, their team, their environment, and their tools; and they live to live an appropriate set of Values.

Culture and shared meaning are a real challenge for Quality Engineering wishing to animate a transversal customer-oriented dynamic.

#4 Manage organizational knowledge

Collecting, maintaining and disseminating knowledge is a challenge for organizations.

As for customers, “ Anywhere, Anytime “ also applies to access to information.

The growing complexity of organizations does not simplify the task of Quality Engineering, which often has to navigate between different and poorly integrable sources.

With a little luck, the company will have capitalized on a portal for a minimum of product documentation.

The knowledge issue is broader, encompassing both functional and technical issues.

Stakeholders also want usable formats, adding a visualization issue.

To make the task more complex, knowledge is increasingly dynamic.

#5 Develop Analytics and Observability

Processes are alive, evolving and must be observed in motion.

The field of Analytics is about creating value by analyzing data, reports and other metrics.

It supports better quality decision-making, based on history and increasingly on forecasts.

Observability focuses on the perception of the state of a system by analyzing its attributes and data accessible externally.

Concretely, do you know how to observe your different digital customer experiences in real-time?

The complexity of uses, organizations and technologies most often results in a partial view of the processes.

The emergence of Process Mining, Data Mesh architectures and metric standards, events are helping us.

In the meantime, Analytics and Observability remain real challenges for Quality Engineering, which will often have to cross several reports.

#6 Deploy automation

Beyond the technological challenge, automation presents real topics of adoption and state of mind.

One might ask: “Why not automate everything?”

Balance, relevance and common sense must be considered.

Tasks can be temporary and unstable making their automation unnecessary.

Some processes may require regular adaptations before they are robust. In addition of a high cost of rework, the delay of feedback loop slows down the improvement iterations.

Keep in mind the Bill Gates quote about the scale inefficiency of automating a bad process.

For other tasks, automating is just not possible yet.

I will pass you the challenges inherent in automation such as training, the technological implementation of RPA, or finding the necessary profiles.

Quality Engineering is to enable a fair adoption and transferring the value of automation at the company’s service.

#7 Speed ​​up software delivery under constraints

A developer must be able to deliver a line of code that the customer can use in a matter of minutes.

All this while ensuring 24/7 availability, multi-country, on various smartphones and devices, security, scalability, among others.

It is with these constraints that the complex balance of acceleration and control of the quality of software.

Processes often reveal their limits when taken to their extremes, a situation increasingly common in companies.

Quality Engineering must succeed in being at the service of an efficient development flow to make their lives easier.

Provide a real development platform allowing to deliver, validate and react quickly emerged in the concepts of Developer Platform, DevEx, Engineering Productivity.

Even if No-Code or Low-Code is on the rise, it won’t solve all of our problems, development experience, or DevEx, remains a major subject.

#8 Maintain a consistent DevEx between environments

To meet our various constraints, distributed architectures and test environments have emerged.

On the other hand, we have created other problems: the coherence of the various environments between them.

Test Environment Management (TEM) and Test Data Management (TDM) are too often underestimated.

The use cases have been detailed, why worry about the environment and the datasets?

To guarantee the value-added of the teams.

Even with the best trendy IDE or Cloud solutions, a developer will be unproductive if he loses 4 hours trying to integrate his development, for each change.

The integration and consistency of environments is the main subject for Quality Engineering, which increases the performance of teams.

#9 Gradually adopt AI

Let’s finish with our last bullet, the famous Artificial Intelligence (AI).

Difficult to identify the projects that have gone to production with so much enthusiasm for the subject.

Its goal is to automate complex and reactive decision matrices at scale.

Setting up real AI processes is an advanced form of automation, more complex organizationally, technologically, but also humanely.

We would like to have a Quality Engineering AI making the best recommendations and adjustments to developments.

We are still a long way due to the immaturity of AI on development processes and operationalization challenges.

We need to keep hope and as with Observability, focus on a more restricted and defined scope, with sufficient data.

What probability of survival for Quality Engineering?

The survival of companies will largely depend on their ability to create efficient organizational systems, beyond technological change.

This is why I believe in an enterprise Quality Engineering, transverse and contributor of value on all the processes and customer experiences.

The challenges identified remain valid, knowing them is the first step in adapting your approach.

A capacity to adapt, which, like humans, improves our chances of survival.

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Originally published at on May 28, 2021.



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