Software Testing Schedule Prediction via SDLC Factor Analysis
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Solution Overview
Problem
Current software testing and Quality Assurance processes are inefficient due to the lack of consideration for SDLC factors like delayed code drop, application downtime, environment downtime, scope changes, and retesting, leading to inaccurate testing schedules, increased costs, and unstable deployed applications.
Innovation Solution
A system and method that includes a user interface for creating a base execution plan, a schedule prediction module to analyze and modify plans based on SDLC factors, and a stability module to determine application stability, allowing for simulation of scenarios and calculation of risk and stability metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional software test management tools are used to predict testing schedule, then test execution metrics can be tracked, but SDLC factors like delayed code drop, application downtime, environment downtime, scope changes and retesting are not considered, leading to inaccurate predictions
Solution Approach 1:
The system segments the testing schedule prediction by dividing it into multiple independent components: base execution plan, SDLC factors (delayed code drop, application downtime, environment downtime, scope changes, retesting), and test execution metrics. Each component is tracked and analyzed separately, then integrated to provide comprehensive predictions.
Solution Approach 2:
The software test management tool is enhanced with multi-functionality to simultaneously track traditional test execution metrics and multiple SDLC factors. The system universally monitors diverse elements (code drop timing, downtime events, scope changes) within a single integrated platform, eliminating the need for separate manual tracking processes.
2Reliability
If manual monitoring and tracking of SDLC factors is performed, then impact assessment can be attempted, but the process is cumbersome and often impossible to maintain accurately on a day to day basis
Solution Approach 1:
The system implements self-service by automatically capturing and monitoring SDLC factors through integrations with development and deployment tools. The system autonomously tracks events like code drop timing, application downtime, and environment availability without requiring manual intervention, thereby maintaining high reliability while ensuring ease of operation.
Solution Approach 2:
The system continuously monitors SDLC factors and provides real-time feedback on their impact on the testing schedule. This feedback mechanism automatically adjusts predictions based on actual events, enabling reliable day-to-day tracking without manual effort. The feedback loop ensures that the system adapts to changing conditions dynamically.
3Productivity
If testing schedule is planned without considering dynamic SDLC factors, then initial planning can be completed, but the schedule becomes inefficient causing delays, higher costs and inadequate testing
Solution Approach 1:
The system performs preliminary action by pre-calculating the impact of various SDLC factors on the testing schedule. During the planning phase, it simulates different scenarios (delayed code drop, extended downtime, scope changes) and adjusts the baseline schedule accordingly. This proactive approach prevents delays by incorporating potential disruptions before they occur.
Solution Approach 2:
The testing schedule is made dynamic by continuously adjusting it based on actual SDLC factor values. As events like code drop delays or downtime occur, the system automatically recalculates the schedule to reflect current conditions. This dynamic adaptation ensures optimal testing efficiency while minimizing delays caused by unforeseen events.
Data Source
AI summary
A system and computer-implemented method for efficiently predicting software testing schedule of one or more applications is provided. The system comprises a user interface configured to facilitate creating a base execution plan for software testing of an application and receive values of one or more factors affecting test execution. The system further comprises a test management tool interface configured to receive data related to test execution from one or more test management tools. Furthermore, the system comprises a schedule prediction module configured to analyze the received values of one or more factors affecting test execution and further configured to modify the created base execution plan based on the received data related to test execution and the analyzed values of the one or more factors affecting test execution to generate one or more modified execution schedules.