If you ’ ve built out your test suite and are now running examination, you ’ re well on your way to embark quality software. But scarper all those tests signify you ’ re also generating a ton of data about trial performance, run time, job history, errors, and failure, all of which guide time to analyze.

According toCapgemini ’ s 2020 Continuous Testing Report, engineering teams pass 44 % of their clip generating, explore, and managing test information - that ’ s 17.6 hr per team member every week.

With a limited act of hours in the day and a never-ending lineup of priorities, you ’ ll need a defendable scheme and best-of-breed tool to uncover the about meaningful penetration in the little period of time. Only then can your team get sound business decisions that prevent costly defects and glitch, application crashes, and even downtime.

In this blog post, you ’ ll find tips for separating the signal from interference. Keep reading to discover how to understand and leverage your datum to do an impact — without exhaust your team ’ s resources.

1. Prioritize Test Cases Across the SDLC

The challenge this solves:Identifying and order matter is time-consuming due to their complex nature and the eminent book of tryout data.

Leveraging test data and insights will facilitate guarantee you take a strategical approach to software development that result in operational efficiency and testing effectiveness.

To commence off, use historic data to prioritise exam across the software development life round (SDLC). Focusing your team ’ s efforts is essential for hazard assessment, resource apportionment, and continuous betterment.

You can break down exam case intotypes of testinglike,,,, and across the various stages of package development. You might prioritize functional tests earlier in the development process, for model, which would permit you place, iris, and fix issues with few people involve.

No topic which approach you conduct, it & # x27; s crucial to properly chase your tests and prioritize your exploit to ensure your package meets prime expectations and bugs don ’ t escape into production.

2. Accelerate Debugging with Root Cause Analysis

The challenge this work:Shallow information and setting get it difficult to regain and multiply subject.

The third most mutual cause of downtime is software bugs, according to a 2022 study byInfonetics Research. In fact, one-third of respondents said bugs had get failures or clang in their application.

To enhance your power to obtain contextual details about test fault, without the demand to reproduce them manually, reckon using video and screenshot log data. Video and screenshot log data can be used to place issues easily and speed up thedebugging process. 

It ’ s crucial to identify and both flaky examination and failures. Although both are vital to maintaining calibre package, they require different approaches, both of which can be supported by collect, dissect, and taking activeness on comprehensive test data.

3. Dive Deep into Deceptive, Flaky Tests

The challenge this solves:It ’ s challenging to identify the source of an issue, leading to unpredictable and unreliable, eccentric tests.

A flaky tryout happens when the result is inconsistent or the source of the failure can not be identified. Not merely is this misleading and deceptive, but it can be a Brobdingnagian time suck.

One of our customers said it best: “ A flakey test is most as useless as having no exam at all. It & # x27; s peradventure still worse because we spend so much clip on something that might not be an issue. ”

To palliate this risk, you can use information to analyse a flaky test to understand what & # x27; s creating the craziness. Possible causes include issues in the environment, shape setup, coating changes, software bugs, latency issues, automation maintenance, or something else entirely.

Use your historical exam data, logs, outputs, tendency, and fault and elision data to analyze freaky test thoroughly, collaborating with your team to expose the subject. Determine what is neglect, where, when, and why.

Once you see the ground behind gonzo tests, you can work to reduce them.

4. Analyze Your Test Failures

The challenge this solve:Taking clip to identify failed tryout takes away from the clip you could expend bump and fixing bugs.

Failures, on the other mitt, indicate ordered issues with the code and test cases. In other words, they are easier to prescribe than flaky tests.

Using comprehensive test datum, you can uncover failure patterns within your testing suite to streamline issue catching and triage of the almost pervasive errors.

A failure analysis tool can aid you decipher where test playscript are broken and what precisely ask to be mend. With that, you can review and analyze the test pass and fail data to identify issues that impact the overall exam suite.

Some tools can yet be used to streamline issue catching by using AI to better developer efficiency and get to market faster with higher-quality software.

Regardless of the method, analyzing test data is critical to identifying subject early, fixing problems, and delivering optimal software.

5. Slice the Data for More Informed Decision-Making

The challenge this solves:It ’ s unmanageable to drill-down on company-specific data around coverage, resources, test effectiveness, and team productiveness.

There are many ways to slice and examine your test data. Making this activeness a habit can merely benefit you.

More ofttimes than not, you can analyze tests directly within your test platform and look at the data by time frame, device, team extremity, or other variables. However, you can besides extract the data and practice more in-depth dissection via API, webhooks, or even spreadsheet software. The possibilities are endless.

You might slice your test data by eccentric (Android versus iOS) to realize how your tests perform across mobile device. Or, you might explore how failure related to deployment or regressions are caused by new features.

SUSA automates exploratory testing with persona-driven behavior, catching bugs that scripted automation misses.

From there, you can create a report, manipulate the data in a way that meets your needs, clearly articulate your test process strategy, and make more informed decisions.

6. Visualize Information in Custom-Built Dashboards

The challenge this solves:You lack the power to ply comprehensive exam insights and an overview of tendency to leaders in an piquant way.

Tailoring fascia to different groups based on their need, data uptake wont, and goals is crucial. For leadership, aim to create dashboard that render an overview of metrics, such as test executing, failure, pass-rate part, and overall tryout coverage.

More advanced data visualisation might require exporting data to a BI tool, which can also be done via webhooks or APIs. Custom-built dashboard help you measure performance, identify issues, and demonstrate the team & # x27; s work in a visually engaging way, making it easy for leadership and cross-functional teams to comprehend.

& quot; People are more worn to colorful, flashy charts than inclination and numbers, & quot; said a Sauce Labs customer from a take tax preparation supplier.

7. Share Data-Driven Insights Across Functions to Drive Alignment

The challenge this solves:External teams are unaware of the impact and importance of test mechanization because it & # x27; s hard to share with others in a digestible formatting.

Whether you ’ re sharing with other QA teams, developer or engineers, or yet product, it ’ s important to expose test data, create dashboards, and build reportage for alinement and collaboration. With this approach, you can cite the like datasets to make decisions, increase the visibility of test history and performance, and control information is easily approachable.

8. Report on Critical Metrics

The challenge this lick:You need to regularly define, track, and report on key metrics around exam and team performance.

When reporting on test data, metrics, and KPIs, it & # x27; s important to first understand your organization & # x27; s high-level business goals and initiatives.

For example, your business might prioritize outstanding customer experiences, which are power by reliable applications. So, your team might be creditworthy for secure that defects are not released into production. Some critical prosody that drive this include failures, bugs and flakiness, all of which are significant to leadership. From thither, you can likewise measure and report on leading indicators like run time, device reportage, and time spent canvas failures.

To align the work your team is perform with organizational outcomes and prove your squad ’ s return on investment, it & # x27; s important to lead the time to measure and understand how testing wallop speed, time-to-release, and customer experience.

9. Uplift Your Team ’ s Performance and Productivity

The challenge this work:It ’ s difficult to understand blocker without the ability to view individual and team testing metrics.

Using data like trial tendency and usage analytics, you can set finish for your team and leverage the dashboards to guide regular follow-ups and check-ins. Take advantage of these data-driven splasher to mensurate performance, team productiveness, country of improvement, and even release identification.

You can uncover opportunities for optimization by comparing current test run-times to a baseline, detecting any anomalies that may indicate potential problems. For example, if your current tests take 2X long than your baseline, there is likely a trouble. Use this info to investigate whether extended execution times are due to increased coverage, slower measure, or specific issues, and then address them collaboratively within the squad.

10. Integrate still more data to unlock greater test observability

The challenge this solve:You need to aline your try strategy and decision qualification with extra datum points across the SDLC as well as information about exploiter experience.

As you begin to grasp the uncommitted data, the following footstep is to bring extra root together to get a more holistic perspective. Use external data by wreak it into your essay program, something you can do habituate pre-built integrations. For instance, when looking at device coverage, measure the virtually popular device that your customers use and align this datum to prioritize the device your teams essay on. You can do this by triangulating data from Google Analytics to see the exact number of user and their near popular devices and browsers, so get informed decision about how to optimize your access to testing.

Take Control of Your Test Data and Generate Meaningful Test Insights with Sauce Labs

Harnessing the power of your exam data is not precisely about collecting information — it & # x27; s about making informed determination, motor productivity, and ensuring the reliability and quality of your software.

With these gratuity, you can navigate the vast landscape of data generated by your test efforts. Prioritizing test cases, accelerating debugging, addressing flaky examination, and utilizing custom dashboards are just a few steps toward efficiently unlocking the full potential of your data. By partake insights cross-functionally, reporting on critical metrics, and integrating additional data sources, you can additionally make a culture of data-driven excellence within your squad.

Sauce Labs is your partner in software lineament. By scat all of your tests on Sauce Labs, your raw data lives in one central place, which can be leveraged to analyze and visualize insights and create timely, data-driven conclusion. We ’ re here to help you embrace the wealthiness of datum within the Sauce Labs program, guiding you towards more efficient, authentic, and high-quality application development.

provides a set of data-rich lineament like Usage Reporting, Failure Analysis, Jobs Overview, and more to enhance your screen efficiency, execution, and reliability. Leverage actionable perceptiveness and embrace the wealth of data within the Sauce Labs program that can guide you towards high-quality coating ontogenesis.