Application Performance Testing System for Network Issue Identification
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Solution Overview
Problem
Mobile applications experience slowdowns, failures, and adverse user experiences due to network conditions, and traditional analysis methods are complex and insufficient for identifying and addressing issues related to network communication, making it difficult for developers to improve application performance.
Innovation Solution
A system that processes and presents data traffic between an application under test and other devices to identify causally related activities, generating burst and issue data, which is then displayed in a user-friendly interface to help developers correlate performance issues with specific activities and suggest improvements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional analysis methods are used to identify network communication issues, then developers can detect problems, but the analysis process becomes complex and insufficient for identifying and addressing issues efficiently
Solution Approach 1:
The system segments network communication analysis into distinct components: data traffic capture, activity identification, causal relationship detection, and issue generation. Each component handles a specific aspect of the analysis, making the overall complex process manageable and systematic while improving identification accuracy.
Solution Approach 2:
The system introduces an intermediary analysis layer that processes raw data traffic between applications and networks. This intermediary layer identifies causally related activities and generates structured issue data, bridging the gap between raw network data and actionable insights for developers without requiring them to directly analyze complex traffic patterns.
2Reliability
If comprehensive data traffic analysis is performed to identify all network-related issues, then issue detection capability improves, but the time and resources required for analysis increase
Solution Approach 1:
The system performs preliminary actions by automatically capturing and preprocessing data traffic during application operation. It pre-identifies causally related activities and prepares issue data in advance, so that when developers need performance assessments, the analysis is already complete or near-complete, significantly reducing the time required for reliable performance assessment.
Solution Approach 2:
The system enables self-service automated analysis that continuously monitors and analyzes network communication without requiring constant developer intervention. The automated identification of causally related activities and generation of issue data allows the system to serve itself in performing comprehensive analysis, improving reliability while minimizing the time developers need to spend on manual analysis.
3Measurement precision
If detailed activity data is collected to correlate performance issues with specific activities, then issue correlation accuracy improves, but data processing complexity increases
Solution Approach 1:
The system segments detailed activity data into distinct, identifiable units and correlates each with specific performance issues. By breaking down the correlation process into manageable segments of causally related activities, the system achieves high correlation precision while keeping data processing complexity organized and systematic rather than overwhelming.
Data Source
AI summary
Mobile devices worldwide execute applications that utilize data services, with issues involving the transfer of data via networks impacting the operation and user experience of those applications. Data is acquired from a mobile computing device executing an application and processed to determine occurrence of a group of related activities performed when executing the application. Parameters of the activities are analyzed, and those parameters associated with poor performance are presented in a user interface. The interface provides impact information about the effect of the activities on operation of the application and may include recommended actions to mitigate the poor performance. A user may interact with data within the interface to trigger a workflow to analyze indicated portions of the data. Subsequent results of this analysis may be returned to the user via the user interface or may be implemented as analytic rules for subsequent data processing.