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4 “No Fail” Best Practices for Enhanced Application Diagnostics

Many things can, and will, go wrong during the development of an enterprise application. These issues underscore the importance of using test cycles to detect potential performance-robbing defects before the application is moved into production. To combat the myriad problems waiting to plague the application lifecycle, developers need to equip themselves with items they can employ to troubleshoot such issues when they arise.

User Acceptance Testing, in theory, is used to utilize the end-user to test the application before it moves on to other stages of the application lifecycle and give their approval, but rarely does an application become exposed to the variety of situations it will experience in production.

Too often, the underlying middleware message layer is often regarded as a “black-box” during this process. And that’s ok, as long as there are no problems. Testers know how long a message or transaction took to transit that layer or the architecture. But if that took too long or was routed to the wrong location, they don’t know why.

This lack of visibility can make it rather difficult to reproduce and then resolve a production problem. Lack of visibility also forces development to manually contact the middleware administrator in shared services and request information about message contents. Certainly, this is an interruption to the middleware administrator and a very inefficient, costly and error-prone process.

As more firms move to a DevOps culture, cooperation in usage of tools across development and production is important. At the very least it gets the two teams speaking the same language, which means time spent in trying to reproduce a problem that is adequately specified can be reduced. At best it can help the joint teams rapidly identify a problem, reproduce it in the test cycle and then develop a resolution.

The following “no fail” best practices are designed for Independent Software Vendors to enforce consistent guidelines for application, middleware and transaction diagnostics in order to rapidly identify, trace, replicate and resolve issues that occur during production.

1. Visibility

Ensure that you have the most detailed visibility into the performance of your applications as possible. Synthetic transactions are not enough. Detailed diagnostics down to the message contents or method level are essential -- you need to see more than just what is being passed into and out of an application as if it were a “black box.”

Instead, ensure you have full visibility of each message and transaction. Use diagnostics at each juncture to proactively provide detailed information when an application’s behavior veers from the expected.

2. Traceability

Knowing when a metric has been breached is an important first step in optimizing application performance during test cycles. Knowing exactly what caused the problem is more challenging. Traditional testing methodologies treat the symptoms looking outside-in, not the root cause which often requires an inside-out viewpoint. Make certain that you can trace the message path in its entirety to uncover the precise moment and environment when the problem occurred.

3. Reproducibility

The key to any successful testing program is the ability to reproduce an error. It is the confirmation of a problem solved, and guarantees that the same problem will never need to be resolved twice.

4. Actionability

Once a problem and its trigger have been identified, and after it has been successfully isolated through replication, developers have all of the tools they need to confidently act on the information and permanently resolve application performance problems. This means they need the tools to -- on their own -- create new messages, re-route them and test their problem resolution.

The ability to identify problems sooner in the application lifecycle will yield better results when the need to remediate issues arises in production. This can only happen when development and production are working together as a team, utilizing a common tool set and when development is enabled with full visibility. This approach will save time and money, as well as helping organizations meet SLAs and drive ROI from these applications.

About Charley Rich

Charley Rich, Vice President of Product Management and Marketing at Nastel, is a software product management professional who brings over 20 years of experience working with large-scale customers to meet their application and systems management requirements. Earlier in his career he held positions in Worldwide Product Management at IBM, as Director of Product Management at EMC/SMARTS, and Vice President of Field Marketing for eCommerce firm InterWorld. Charley is a sought after speaker and a published author with a patent in the application management field.

Related Links:

www.nastel.com

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4 “No Fail” Best Practices for Enhanced Application Diagnostics

Many things can, and will, go wrong during the development of an enterprise application. These issues underscore the importance of using test cycles to detect potential performance-robbing defects before the application is moved into production. To combat the myriad problems waiting to plague the application lifecycle, developers need to equip themselves with items they can employ to troubleshoot such issues when they arise.

User Acceptance Testing, in theory, is used to utilize the end-user to test the application before it moves on to other stages of the application lifecycle and give their approval, but rarely does an application become exposed to the variety of situations it will experience in production.

Too often, the underlying middleware message layer is often regarded as a “black-box” during this process. And that’s ok, as long as there are no problems. Testers know how long a message or transaction took to transit that layer or the architecture. But if that took too long or was routed to the wrong location, they don’t know why.

This lack of visibility can make it rather difficult to reproduce and then resolve a production problem. Lack of visibility also forces development to manually contact the middleware administrator in shared services and request information about message contents. Certainly, this is an interruption to the middleware administrator and a very inefficient, costly and error-prone process.

As more firms move to a DevOps culture, cooperation in usage of tools across development and production is important. At the very least it gets the two teams speaking the same language, which means time spent in trying to reproduce a problem that is adequately specified can be reduced. At best it can help the joint teams rapidly identify a problem, reproduce it in the test cycle and then develop a resolution.

The following “no fail” best practices are designed for Independent Software Vendors to enforce consistent guidelines for application, middleware and transaction diagnostics in order to rapidly identify, trace, replicate and resolve issues that occur during production.

1. Visibility

Ensure that you have the most detailed visibility into the performance of your applications as possible. Synthetic transactions are not enough. Detailed diagnostics down to the message contents or method level are essential -- you need to see more than just what is being passed into and out of an application as if it were a “black box.”

Instead, ensure you have full visibility of each message and transaction. Use diagnostics at each juncture to proactively provide detailed information when an application’s behavior veers from the expected.

2. Traceability

Knowing when a metric has been breached is an important first step in optimizing application performance during test cycles. Knowing exactly what caused the problem is more challenging. Traditional testing methodologies treat the symptoms looking outside-in, not the root cause which often requires an inside-out viewpoint. Make certain that you can trace the message path in its entirety to uncover the precise moment and environment when the problem occurred.

3. Reproducibility

The key to any successful testing program is the ability to reproduce an error. It is the confirmation of a problem solved, and guarantees that the same problem will never need to be resolved twice.

4. Actionability

Once a problem and its trigger have been identified, and after it has been successfully isolated through replication, developers have all of the tools they need to confidently act on the information and permanently resolve application performance problems. This means they need the tools to -- on their own -- create new messages, re-route them and test their problem resolution.

The ability to identify problems sooner in the application lifecycle will yield better results when the need to remediate issues arises in production. This can only happen when development and production are working together as a team, utilizing a common tool set and when development is enabled with full visibility. This approach will save time and money, as well as helping organizations meet SLAs and drive ROI from these applications.

About Charley Rich

Charley Rich, Vice President of Product Management and Marketing at Nastel, is a software product management professional who brings over 20 years of experience working with large-scale customers to meet their application and systems management requirements. Earlier in his career he held positions in Worldwide Product Management at IBM, as Director of Product Management at EMC/SMARTS, and Vice President of Field Marketing for eCommerce firm InterWorld. Charley is a sought after speaker and a published author with a patent in the application management field.

Related Links:

www.nastel.com

Hot Topics

The Latest

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...