Ace ERP Testing with "EvoTest": Your AI-Powered Evolutionary Solution

ERP testing can be a daunting task. But what if you had an AI assistant that constantly learns and adapts, evolving alongside your testing needs? EvoTest is that solution.

Here's how it tackles the five key challenges:

1. Self-Evolving Test Data Management:-

Adaptive Data Synthesis with Reinforcement Learning: EvoTest utilizes cutting-edge reinforcement learning to analyze test results and user feedback. This allows it to continuously learn and improve the data generation process. Imagine EvoTest creating increasingly realistic test scenarios based on past test failures, ensuring edge cases are constantly covered.

Dynamic Data Augmentation: EvoTest doesn't just generate data. It utilizes AI to dynamically augment existing data with realistic variations and anomalies. This includes simulating unexpected user behavior, network disruptions, and other potential challenges, ensuring comprehensive test coverage.

2. Predictive Defect Identification with Explainability:-

Multi-source Defect Prediction: EvoTest goes beyond historical data. It incorporates diverse sources like code analysis, user feedback, and real-time system logs for a comprehensive view. This multi-source approach improves the accuracy of defect prediction.

Explainable AI for Prioritization: EvoTest doesn't just predict defects; it explains them. The "Why" behind the prediction empowers testers to prioritize effectively. Imagine EvoTest highlighting a specific line of code as a potential cause of a predicted defect, allowing developers to address it directly.

3. AI-powered Test Automation Suite:-

NLP-driven Smart Test Case Generation: EvoTest utilizes advanced NLP to analyze not just user stories and requirements but also past test case successes and failures. This allows it to generate highly efficient test scripts that target potential weaknesses, maximizing test coverage.

Cognitive Test Automation Framework: Traditional automation scripts become brittle over time. EvoTest employs a cognitive test automation framework powered by AI. This framework can not only self-heal from minor UI changes but also learn from successful test runs, continuously improving its efficiency.

4. Collaborative Testing Ecosystem:-

AI-powered Testing Hub with Gamification: EvoTest fosters collaboration through a central AI-powered hub. This hub provides:

Interactive Insights Dashboard: Real-time test execution reports with actionable insights based on AI analysis and historical data, visualized in a user-friendly format.

Conversational Testing Assistant with Gamification: A built-in conversational AI assistant, "Test Sensei," guides testers, resolves issues, and offers suggestions based on historical data and best practices. Gamification elements like leaderboards and badges promote healthy competition and knowledge sharing.

5. Continuous Learning and Improvement:-

Community-driven Knowledge Base: EvoTest leverages a community-driven knowledge base. Testers can contribute their learnings and insights, constantly enriching the system's knowledge and improving its future predictions and recommendations.

Regular AI Model Updates: EvoTest's AI models are continuously updated and improved based on new data, user feedback, and advancements in AI research. This ensures the solution stays at the forefront of ERP testing technology.

Proof-of-Concept: Explainable Prioritization

Imagine testing a new ERP module for inventory management. EvoTest analyzes code and historical data, predicting a potential defect in stock level calculations. The "Why" explanation highlights a specific mathematical formula within the code. Knowing the root cause, developers can prioritize fixing the formula, ensuring accurate inventory management before deployment.

EvoTest: Your Advantage

By leveraging the power of evolving AI, EvoTest empowers you to:

Outpace Testing Challenges: Continuously learn and adapt to evolving testing needs.

Reduce Risks Proactively: Predict defects with explainability, enabling targeted fixes.

Boost Collaboration and Knowledge Sharing: Foster a collaborative testing environment.

Optimize Efficiency and Test Coverage: Automate tasks and generate highly efficient test data and scripts.

EvoTest is your key to achieving successful ERP implementations through a constantly evolving and adaptive AI-powered testing solution. 

Strata: A Multi-Layered AI Approach

ERP testing can be a labyrinth of complexities. Strata cuts through the maze with a multi-layered AI solution that streamlines processes and empowers informed decision-making. Here's how Strata tackles the five key challenges:

 

1.Context-Aware Test Data Generation

Domain-Specific Knowledge Integration:- Strata goes beyond simple data generation. It leverages Natural Language Processing (NLP) to analyze user roles, business processes, and domain-specific terminology within the ERP system. Imagine simulating a purchase order with realistic product details, pricing, and approval workflows specific to your industry.

Adaptive Mutation Engine:- Strata employs a cutting-edge mutation engine powered by advanced AI. This engine injects realistic anomalies and unexpected user behavior into test data, mimicking potential disruptions like network issues or hardware failures. This ensures comprehensive testing for even the most unexpected scenarios.

2.Multi-Faceted Defect Prediction with Confidence Scoring

Heterogeneous Data Fusion:- Strata doesn't rely solely on historical testing data. It utilizes a sophisticated AI model that merges diverse data sources including code analysis, user feedback, and real-time system logs. This holistic approach provides a more comprehensive picture of potential defects.

Confidence-Scored Defect Prioritization:- Strata goes beyond simple defect prediction. It assigns a confidence score to each prediction, indicating the likelihood of the defect being real. This empowers testers to prioritize critical issues with the highest confidence levels for maximum impact.

3. AI-powered Test Automation with Self-Healing Capabilities

NLP-driven Test Case Generation with Explainability:- Strata utilizes NLP to analyze user stories, business requirements, and existing test cases. It not only generates individual scripts but also offers explanations for the generated steps. This allows testers to understand the reasoning behind the tests and adapt them if needed.

Cognitive Test Automation with Self-Healing:- Strata's test automation framework utilizes AI for self-healing capabilities. It can automatically adjust to minor UI changes in the ERP system, ensuring the continued effectiveness of your automated tests without the need for constant manual updates.

4. Collaborative Testing Hub with Knowledge Sharing

Interactive Insights Dashboard with Actionable Recommendations:- Strata's central AI-powered hub provides real-time test execution reports visualized in an interactive format. It goes beyond basic data by offering actionable insights based on AI analysis and historical data.

Conversational Testing Assistant with Community Forum:- Strata fosters collaboration through a built-in conversational AI assistant, "Test Navigator." Test Navigator can guide testers, answer questions, and suggest solutions based on historical data and best practices. Additionally, a community forum allows testers to share knowledge and best practices, fostering continuous learning.

5. Continuous Learning and Improvement

 Federated Learning for Decentralized Knowledge Sharing:- Strata utilizes a federated learning approach. This allows secure knowledge sharing between different deployments of Strata without compromising sensitive data. This shared knowledge pool continuously improves the AI models deployed across various organizations.

Regular AI Model Updates with Explainable Enhancements:- Strata's AI models are continuously updated and improved based on new data, user feedback, and advancements in AI research. Additionally, new enhancements are explained in an understandable way, empowering users to trust the AI's decision-making.

Proof-of-Concept: Confidence-Scored Prioritization

Imagine testing a new ERP module for financial transactions. Strata analyzes code and historical data, predicting a potential defect in currency conversion calculations. However, the confidence score assigned is relatively low. This allows testers to prioritize a different defect with a high confidence score related to user authentication, ensuring critical issues are addressed first.

Strata: Your Competitive Edge

By leveraging a multi-layered AI approach, Strata empowers you to:

Achieve Superior Test Coverage:- Context-aware data generation and adaptive mutation engines ensure comprehensive testing of realistic scenarios.

Focus on High-Impact Issues:- Confidence-scored defect prediction allows for prioritizing critical defects with the highest likelihood of causing problems.

Boost Collaboration and Knowledge Sharing:- The collaborative testing hub fosters a culture of learning and continuous improvement.

Reduce Manual Work and Optimize Efficiency:- Automate tasks like test data generation and case creation, freeing up resources for more strategic activities.

With Strata, gain a competitive edge in your ERP testing and ensure a smooth and successful implementation. 

Imagine a Sherpa for your ERP testing journey. Not just a guide, but an AI-powered assistant that anticipates challenges, optimizes your path, and equips you to conquer the peak of successful implementation.

 Sherpa - The ERP Solution:

• Test Data Management:
  • Leverage AI to analyze historical data and industry trends, predicting potential data anomalies and security concerns.
  • Utilize natural language processing (NLP) to understand your specific business needs and generate tailored test data sets that reflect real-world scenarios.
• Predictive Test Case Prioritization:
  • Train Machine Learning (ML) models on historical testing data to identify areas with the highest risk of defects.
  • Integrate with business intelligence tools to understand the criticality of different functionalities to your operations.
  • Dynamically prioritize test cases based on risk scores, business impact, and real-time test results, ensuring you address the most critical issues first.
• Intelligent Test Automation:
  • Implement AI-powered tools that can learn from existing test scripts and user actions, automating repetitive tasks and creating new test cases on the fly.
  • Utilize self-healing algorithms to identify and fix minor errors within automated scripts, minimizing downtime and ensuring smooth test execution.
• Collaboration and Insights:
  • Foster collaboration through a centralized platform where testers, developers, and business stakeholders can access real-time testing data and insights.
  • Employ NLP-powered chatbots to answer tester queries, provide contextual help, and facilitate knowledge sharing within the team.
  • Leverage AI to generate automated reports with visualizations highlighting potential defects, root cause analysis suggestions, and recommendations for improvement.

Benefits of Sherpa : 

• Predictive Maintenance: 

AI analyzes testing data and user behavior patterns to identify potential system issues before they occur. This allows for proactive maintenance and prevents disruptions during ERP implementation.

• Continuous Learning: 

The ERP Sherpa continuously learns and evolves based on testing results and user feedback. This ensures the system adapts to changing business needs and testing methodologies.

• Reduced Risk and Costs: 

Proactive risk identification and intelligent test prioritization minimize the chance of critical defects slipping through, leading to cost savings and a smoother implementation.

• Improved Quality and Agility: 

AI-powered data generation, automation, and insights ensure comprehensive testing, enabling faster feedback loops and a more agile implementation process.

• Enhanced Collaboration and Transparency: 

The centralized platform and AI-powered communication tools break down silos and foster collaboration between all stakeholders.

• Centralized Platform: 

Sherpa  provides a centralized platform where testers, developers, and business stakeholders can collaborate seamlessly. This platform serves as a hub for sharing information, discussing issues, and tracking progress, fostering a cohesive team environment.

• Real-Time Data Access: 

With Sherpa , stakeholders have access to real-time testing data and insights. This ensures that everyone is working with the most up-to-date information, facilitating informed decision-making and coordinated action.

• Automated Reporting:

 Sherpa  generates automated reports with visualizations highlighting potential defects, root cause analysis suggestions, and recommendations for improvement. These reports serve as a common reference point for all stakeholders, enabling them to align their efforts towards common goals.

• NLP-Powered Chatbots:

 Sherpa  employs NLP-powered chatbots to provide contextual help and facilitate knowledge sharing within the team. These chatbots can answer questions, provide guidance on testing processes, and offer assistance whenever needed, enhancing communication and collaboration.

• Conversational Defect Tracking: 

Sherpa  facilitates conversational defect tracking, allowing stakeholders to discuss and resolve issues in real-time. This collaborative approach ensures that defects are addressed promptly and effectively, preventing them from escalating into larger problems later on.

The ERP Sherpa isn't just a tool, it's your trusted companion on the path to successful ERP implementation.

Excited to see the submissions, Thanks to the QA community for participation

ERP Testing AI Model: Defect Prediction & Priority Based Verification

Predicting defects and prioritise verification based on the impact on business processes are important for an effective ERP testing AI model.

Historical Data analysis: 

Historical data holds up many flaws which can help to create an effective test strategy. So, its always beneficial to explore historic data.

There are many tools available online paid & open source for doing “Historical data analysis”. Since ERP applications are always closer to an organisation data and one needs to be cautious while sharing those information to any tool. So, I always advocate of using anything built in-house. 

AI solution (Wish List): 

1. Data Collection: Gather historical data from relevant resources like: Defects, logs, Test Results

2. Data Analysis: Understand the pattern from gathered data

This step will completely depend on 

 - How well the defect template is/was defined in Jira or any other defect tracking tool.

 - Are we tracking defect RCA, environment details and Defect classification (Functional, Performance and Security)

3. Model (Logic): 

- Algorithm to predict defect occurrence. 

- Train the model to learn pattern from past data to make future prediction.

- Test the model with new data to predict the occurrence of defects in newly developed features.

- Continuously monitor the model to check the accuracy.

Prioritise Test Verification 

Test prioritization based on the impact on business processes can be a complex task which involves both manual and automated steps.  

The initial identification and mapping of business process can be manual and which can be used later to train the AI models.

1. Business Process - Identification

a. List out all critical business process within the ERP system.

b. List the dependency/relationship between business processes and modules.

2. Business Process - Impact Analysis

a. For the above identified processes assess the business impact which can include factors like frequency of use, user impact and revenue impact.

b. Use the above data collection to understand which process or module have the most impact when failure occurred.

3. Testcase Identification:

a. Identify the test cases for the above identified business process and list out execution frequency and past failures.

4. Model(Logic)

 - Extend the algorithm to predict the occurrence of defect in identified processes based on the historic data.

 - Based on the defect likelihood develop a scoring algorithm to prioritise the business process related test cases.

 - Focus testing on high priority test cases which has significant business impact.

 - Continuously monitor and retrain the model.

Team Collaboration

A god tool or solution is useless if it is not used correctly. 

For a well defined AI model, collaboration among different team members plays an important role. Here’s how each team member can play their role well.

Business Stakeholders/PO:

They possess valuable domain expertise. Business stakeholders can review AI model outputs to make sure model align with business goals. 

Business stakeholders/PO’s can execute scenarios under various business conditions.

QA:

Provide feedback on model by performing rigorous testing. Identify areas of improvement.

PM:

Being a facilitator they can initiate communication among stakeholders and ensure alignment with project milestone.

ERP Testing with AI-Driven Solutions : 

ERP overview : 

As we know ERP stands for Enterprise Resource Planning which means a software system which integrates various business processes by sharing data and standardising processes, it streamlines internal business processes, improves efficiency, and provides insight into an organisation's operations.

Problem Statement : 

Let’s discuss one Testing challenge in  warehouse management 

• Warehouse management involves a complex process such as Inventory management,order fulfilment, shipment packing, and shipping.
• Maintaining the data with integrity is a challenge as the ERP software in the Integration of different modules .

Featuring the new AI solution : NexTestAI 

Test Data Management : 

• Test data management is important for warehouse management because lots of things depend on warehouse data. For example if we want to maintain a data sheet with various products with current stocks and reorder products once they reach the  threshold value . if the data is incurrent it will impact the production following the delivery and shipments 

AI solution : 

• The NexTestAI  will significantly improves the test data management by using following steps
  •  Data Creation - By generating realtisting data for various operations such as inventory , orders, shipments
  • Regularly updating test data to keep it relevant and ensuring it accurately represents the current operational environment.
  • As AI is smart enough so we can take the advantage in DATA MASKING i.e for testing it can generate data similar to the real data to comply with privacy regulation ( as we know the keeping the data safe in a  BIG Challenge in AI era ) 

Proof-of-concept : 

• As per above explanation it can be achieved by collecting the Historical data ( in warehouse case we can say that products inventory levels, records.
• Generating real data and AI can analyse the historical data and generate real time scenarios.

Scenario -1 : 

• Stock level fall below the threshold point
  • AI will analyse and record the data as expected level = 100 units and current level = 55 units 
  • Action : AI will trigger the reorder and generate the report and send to all stakeholders

Scenario : 2 

• Suddenly requires higher demand of units for Product X
• AI will analyse the Current level = 1078 Units | Required : 89678 Units → hence it will trigger reorder 

Test Case Prioritisation : 

• Using NexTestAI model in the test case prioritisation would be useful as the AI can prioritise the Test cases based on the importance /critical functionality & by considering uncovered defect areas also 

AI solution :

• It will priorities the test cases by analysing the historical data, past execution details, code changes & defect logs 
• The well trained AI model consider &  prioritise the integration cases in the scenarios were the two models are integrated
• By analysing the criticality it Will prioritise the Edge case such as when the product stock levels exactly at the reorder point, when it is at Zero stock 
• By calculating high risk areas and past data it will priorities the test cases ( for example if the reordering calculation is getting incorrect most of the time due to the frequent code changes )

Predict Defects : 

• As we are aware of the complexity of ERP solution which involves various factors, so predicting  a defect is a tough & time consuming task for a tester but we can reduce our effort by leveraging the AI capabilities, 
  •   AI will analyze the frequency of defect coming from pat execution and it will check for the recent code changes with code complexity 
  • By gathering past execution details and historical data and it will predict the similar defects & we can Priorities testing efforts on areas with high predicted defect probability

Test Automation : 

•  It's correct that the automated suit reduces the testers efforts but we all know in the Test Automation optimizing and maintaining the test scripts. 

But we can reduces these challenges using AI - driven Automation suite 

AI solution :

• AI powered tool can understand the requirements,user stories and past execution data also to analyse and generate maximum possible scenarios  
• As it leverage the NLP (Natural Language Programming) to monitor frequent changes and it update the scripts as per requirements to keep the relevant 
• For example : we want to generate the test suite whenever the stock level goes below to a certain threshold point 
  • By using the NLP it will analyse the data and generate the Automated test suit for to test it in various scenarios (e.g At different stock level, At Zero Stock Leve, At different reorder threshold values)
• We can not bypass the major advantage of using AI driven tool to generate Automation suite is Optimization and maintenance
  • Optimization : 
    • It can be achieved by analyzing the Test suite and remove all the redundancies
    • It will insure that the generated test suites covers all the critical/Major/edge cases  
  • Maintenance : 
    • AI can observe the frequent changes in the process (e.g reorder calculation) and update the automated suites accordingly 
    • It will also update the locators and scripts changes when needed 

Collaboration between various stakeholders : 

Just think people from different departments who are involved in ERP testing are able to interact while testing is in Progress or In ERP Lifecycle Process. By doing this we can improve our end product and issues/doubts  can also be resolved in less time.
 

Here is how we can achieve it by incorporating few factors, such as :
 

1. Communication : The AI tool enables the better communication (Real Time) platform among the team members via chats, Notifications, real time data Updates, Tasks updation, track of work
    
2. Information : The AI modal will check and ensure that all the stakeholders are updated with the latest data and information so it will help in minimising the confusion, misunderstanding and various types of defects.
    
3. Reducing the effort in Repetitive tasks : The AI tool will help us in saving our time by automating repetitive tasks, such as task prioritisation based on the urgency. Hence we can focus on some other critical tasks effectively
    
4. Providing reports to Stakeholders : It will generate the reports not only with the DATA but along with the statistics & Analytics and it  will automatically  send it to the stakeholders.

Proof-of-concept : 

By considering all above points we can say that our AI modal will helps us in Collaboration between various stakeholders by integrating ERP platform with realtime communication and various actions 

For example when a tester finds a bug the AI will notify this to related developers and Dev. / Test Manager along with product manager and scrum master so that it will reduce the delay in addressing the Issue.

It will also prioritise the task based on criticality and save our time additionally AI will generate reports like DSR (Daily Status report) , testing Progress and Bug metrics which are accessible to all Stakeholders via a common dashboard.

Thanks
Praveen Kumar

We are excited to announce the conclusion of this month's challenge, created by @sidharth shukla, on how AI can address challenges in ERP testing.

We received several innovative and practical approaches that showcased the potential of AI in revolutionizing ERP testing.

We are also very happy to announce the two winners of this challenge: @Praveen426 and @Praveen 

You will be contacted soon through your ShiftSync Registered emails to discuss the logistics of sending your gifts, so be on the look out for those emails.

Stay tuned for more exciting challenges ahead!

Hello Mustafa
Thank you so much for the great news! It was a fantastic experience, and I'm grateful to @sidharth shukla  and Entire Shiftsync team for the opportunity.

Thanks