Augmented Search for Software Testing

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1 Augmented Search for Software Testing For Testers, Developers, and QA Managers New frontier in big log data analysis and application intelligence Business white paper May 2015

2 During software testing cycles, a massive volume of log data is generated in the testing lab. During software testing, a variety of testing methodologies are used, such as functional, load, regression, manual, and penetration testing. While performing these test cycles, applications generate massive and valuable log data. This log data contains exceptions, events, and reports on quality problems. Big Log Data is a Huge Testing Challenge It is almost impossible to read and review every log line generated during testing cycles. However, errors such as 500, exceptions, AJAX calls that return wrong values, connection failed errors, and many others are in fact logged into the files. How can we harness this data to optimize the application quality? Bugs and Defects Go Undetected Unless we analyze each event in the log data and unlock its full value and meaning, the application will never be fully optimized for production. Analyzing the log data can help us: 1. Create statistics trends, application intelligence, and IT to business correlation. 2. Benefit from application quality and performance quickly find errors, problems, and anomalies. 3. Clean and remove noise from the log data. 4. Isolate problems faster, preventing loss of business. 5. Investigate security and fraud quickly. 2

3 Analyzing Application Data The Applications architecture can be complex, as a single application can be structured from many components. Each component has its unique implementation constraints: language, architecture, and design. Most of the software components generate log data, which is stored in log files, databases, or other data repositories. Each application can also be integrated with either local or remote application services. Augmented Search for Software Testing XpoLog Augmented Search brings new technology and approach to the big data management domain. Augmented Search combines end-to-end log analysis and management platform with cutting edge machine learning and automated Analytics. The new solution delivers: Log management and analysis of data across all sources. Super-fast search for manual investigation and troubleshooting. Automatic visualization of complex log data for reports and dashboards. Proactive monitoring. Automatic Analytics engine, executing many algorithms for machine learning, data mining, statistical analysis, semantic analysis, discovery, and profiling. The result is a built-in intelligence engine that supports user decisions during analysis, proactively adding layers of information and helping to understand data faster and in an automated fashion. This approach is technology oriented and does not require tags, filters, or predefined rules. It supports both homegrown solutions and 3 rd party applications. Augmented Search helps organizations generate ongoing value from log and machine data without manual work. Augmented Search combines a super-fast search with super-smart Analytics. 3

4 Application Log Data Analysis Platform The XpoLog log data analysis platform is built over the following primary components: 1. User interface Web user interface with built-in Tomcat server 2. Dashboards and reports 3. Virtual Data Engine log access, collection, parsing, and management 4. Indexing and Search engine 5. Analytics engines many algorithms that auto generate intelligence This high-level architecture has subcomponents that handle security, proactivity, system health, self-healing system, map/reduce management, connectors, and more. Please visit for more information. Augmented Search Impact Once XpoLog Augmented Search is deployed, all data is collected and analyzed. The following participants experience great value from their use of XpoLog: 1. Support engineers 2. Developers and testers 3. Business application owners 4. Production support 5. DevOps 6. Operations 7. Application infrastructure teams Application data is securely visible to the right people for search and analysis. Auto detected Analytics presents problems, trends, and transactions to users. XpoLog generates live dashboards and reports for ongoing intelligence. Managers can make better decisions with improved understanding of the application state. The XpoLog platform can digest both structured and unstructured data from multiple sources. By indexing and analyzing log and business data, it is possible to create a rich set of intelligence reports, both on common data format and homegrown generated data. Some examples from our customers: 1. Web analytics, trends, and web app usage 2. Trade transaction statistics, volumes, and trends 3. Application feature profiling and statistics 4. Ecommerce website business intelligence 4

5 ROI Augmented Search helps troubleshoot faster and automatically visualizes complex data. With constant proactive Analytics, you can support homegrown solutions and 3 rd party applications, significantly reducing TCO and time-consuming, exhausting manual work. This combination of automated intelligence with super-fast search and dashboards drives efficient out of the box value quickly. Customer Use Cases Searching Application Problems Users can quickly search transaction Ids, users, errors, exceptions, etc. across all log data and sources. They can also search for an IP address and a corresponding user ID, and focus their search query on a specific server or log. Instead of manually connecting to many servers and logs, they can search all the data in one place. Proactive Problems Detection Augmented Search Augmented Search helps quickly find problems in the application tiers. Once a user searches or navigates the application data (web, apps, OS logs, and more), Augmented Search presents a summary of problems and anomalies that were detected in that app's log data. The Augmented Search intelligence layers help focus on the important things first, without the need to read millions of log events. Performance and Analysis Reports With XpoLog Complex Analysis Search, users can visualize data automatically. They can measure response time between different log events, create summaries of thread activity, memory allocations, exceptions, and more. Users can also understand the trends and bottlenecks of their application tiers across the board. 5

6 Summary Augmented Search is a unique technology based on our deep understanding of IT data and organization use cases. This technology helps organizations build a more robust ROI oriented strategy towards big log data in today's data centers. We invite you to read more about technical features in our data sheets and documentation. 6

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