Release Notes - TimeBack Anti-Patterns

2025-09-26

Summary: Antipattern Detection integrated and working in Timeback app. Improvement in Detection Performance. Visibility for prompt engineering.

🚀 What is shipped this week?

• 📈 Performance Achievements:
  • ✅ Achieved 96-98% Accuracy on NonLearningContent antipattern.
  • ☑️ Reached 70% Precision and 70% Recall on EyesOffScreen antipattern.
  • ☑️ Reached 70% Precision and 70% Recall on AwayFromSeat antipattern.
  • Detailed metrics here: Metrics
  • AwayFromSeat prompting doc: AwayFromSeat Prompting
• 🏗️ Integration with Timeback desktop app:
  • The detectors are fully integrated and working in the Timeback app.
  • The desktop app can be used with antipattern detection and dashboard available named as Wastemeter.
• 👨🏻‍💻 Prompt Engineering Visibility:
  • Implementation of mechanism to view LLM thoughts for visiblitiy when prompt engineering peculiar cases.

⏩ What’s coming next?

• Further improvement of detectors.

2025-09-05

Summary: NonLearningContent touches 96-98% Accuracy. Experiment with more segment durations. Bug fixes in test harness.

🚀 What is shipped this week?

• 📈 Performance Achievements:
  • Started prompt improvements iterating with the new setup and new test harness.
  • ✅ Achieved 96-98% Accuracy on NonLearningContent antipattern.
  • ☑️ Reached 65% Precision with 50% Accuracy on EyesOffScreen antipattern.
• ⚗️ Experimenting with more segment durations:
  • Tested the pipeline with more segment durations within 0 to 60 range: 10sec, 15sec, 20sec, 30sec, 45sec, 1min.
  • Performance, memory consumed, and processing time were analyzed.
  • Observations: Memory and latency not an inssue with any duration.
  • Recommendation: Going with 30sec as the default segment duration as the performance dosn’t change noticably.
  • Here’s a detailed report: Experimenting with varying segment durations
• 🐛 Bug Fixes:
  • Calculation of overall accuracy fixed.

📈 What’s coming next?

• 🪑 AwayFromSeat Detection Improvement: With a Target of 90% Precision.
• 👀 EyesOffScreen Detection Improvement: With a Target of 85% Precision.

2025-08-29

Summary: Test Harness Improvements. Experimenting with varying segment durations. Bug Fixes. Minor Performance Improvements.

🚀 What is shipped this week?

• 📊 Test Harness Improvements:
  • Now test harness allows same interval of time to have multiple families associated. (in case of overlaps)
  • Be able to configure which antipatterns to include/exclude in ground truth. Excluded “IDLING” using the configuration. (as that is now programmatically detected)

• ⚗️ Experimenting with varying segment durations:
  • Tested the pipeline with: 10sec, 30sec, 1min, 2min and 5min video segments.
  • Performance, memory consumed, and processing time were analyzed.
  • Here’s a detailed report: Experimenting with varying segment durations
• 🐛 Bug Fixes:
  • Passing proper originating session id to antipatterns detected.
  • Proper handling of originating session id when intervals are merged/splitted.

📈 What’s coming next?

• ⚗️ Experimenting with more segments durations in 0-60 range.
• 🧑🏻‍🎓 Distractions Detection Improvement: With a Target of 85% Precision.
• 🙎🏻 Disengaged Detection Improvement: With a Target of 90% Precision.

2025-08-22

Summary: FamilyType Metric added to test harness. Test Cases fixed. Test Harness revamp complete. Bug Fixes.

🚀 What is shipped this week?

• 📊 Test Harness Improvements:
  • FamilyType Metric added in eventMetrics: Compares TP and FP of a detected event type with the entire family (hence linient) and compare FN with the specific event. This is helpful in knowing if an event is atleast matching the family type even if not classified correctly.
  • Test Cases Fixed. Details here: Buggy Test Cases
  • Linicency window added to test harness (default 7 seconds).
  • Analyzer Dashboard now has updated and corrected metrics.

• 🐛 Bug Fixes:
  • Pipeline failing to run for longer segments.
  • Heap memory issues in github workflow.

📈 What’s coming next?

• 🧑🏻‍🎓 Distractions Detection Improvement: With a Target of 85% Precision.
• ⚗️ Exploring DSPy Framework: Exploring the possibility of leveraging declarative programming to build the detectors.

2025-08-15

Summary: New Output Analyzer and Prompt Improvement Dashboard. Added “Intersection over Union” as an evaluation metric. Improved Distractions Detection. Bug Fixes.

🚀 What is shipped this week?

• 📊 Detection Analyzer Dashboard: An automated dashboard created to analyze the results of the detectors and iterate on them quickly. Gives insights on the performance and probable issues with the detectors. Analyzer Dashboard

• 📈 Added Accuracy as Metric: Researched and added “Intersection over Union” in Test Harness as a metric to evaluate the performance of the detectors. Gives a better idea when working with overlaps of intervals which is our usecase. Also a single interpretable score.
• 🎯 Improved Distractions Detection: Reached 70% Precision and F1 Score.
• 🐛 Bug Fixes: Missing detected events for longer segment duration fixed.

📢 Product Demo and X Posts

• Product Demo: Timeback Hierarchical Antipattern Detection
• X Post: Timeback Hierarchical Antipattern Detection

📈 What’s coming next?

• 🧑🏻‍🎓 Distractions Detection Improvement: With a Target of 85% Precision.
• 🏭 Production Integration: Full-scale deployment of optimized longer video segment processing capabilities.

2025-08-08

Summary: The new framework for evaluation proposed last week is implemented and shipped, the metrics reported with it. Minor improvements in Disengaged antipattern detection and overall detection. Next work is on Distractions and the correct classification of Antipattern types.

🚀 What is shipped this week?

• 📊 New Performance Evaluation Framework Shipped: Last week a detailed documentation was shared of the problems and improvements of the metrics used for evaluation. The new framework discussed is shipped and in use!
• 🔍 Metrics with the new framework on newly annotated videos:
• 🧠 One Detector for all: Single detector detects Distractions and Disengagement Antipattern Families and then detections are classified into Antipattern types. Costs and Latency both saved and works better than individual detectors.
• 👨🏻‍💻 Improved Disengaged Detection: Reached 80% Precision and F1 Score.
• 👨🏻‍💻 Overall Detection improved: Crossed 80% Precision and F1 Score.

📈 What’s coming next?

• 🧑🏻‍🎓 Distractions Detection Improvement: With a Target of 85% Precision.
• 🏭 Production Integration: Full-scale deployment of optimized longer video segment processing capabilities.
• 🧪 Adding Classification Accuracy to evaluate antipattern type detection rather than individual F1, precision and recall.
• 🔬 Prompt Improvement Dashboard quick calculated iterations on the prompt.

2025-08-01

Summary: The Timeback app now wants to make the setting realtime again as opposed to generating reports later for parents. This is being worked at in the production codebase directly, so our earlier pipeline code is now on hold. The architecture and flow is vastly differnt in the production backend, where some optimizations and improvements were made this week.

🚀 Key Deliverables & Achievements

• ⚡ Performance Optimizations: Enhanced development iteration speed in the production codebase. Being a realtime like system, it took about 10 minutes to process a 18 minute video during testing. Now takes under a minute. Modified the flow to be able to utilize concurrency wherever possible.
• 🔧 Critical Bug Resolution: Resolved memory leak vulnerabilities in the core codebase infrastructure. Earlier where 3 minute or longer chunk used to throw out of memory errors, which was due to frame extraction, is now handled gracefully.
• 🔬 Improved Metrics Calculation: Poked some holes in the current way of calculating performance metrics. Details present here: Metrics Calculation
• 🧪 Research & Experimentation: Conducted extensive analysis on variable video segment durations of 10sec, 30sec, 1min, 2min and 5min. It had impacts beyond performance: latency, lambda limits, scalability. Detailed analysis report coming soon.

📈 Strategic Roadmap & Next Milestones

• 🎯 Accuracy Enhancement: Precision improvements, prompt iterations, experiments on models and QC.
• 🏭 Production Integration: Full-scale deployment of optimized longer video segment processing capabilities

2025-07-25

Summary: Integrated our AI processors into the customer’s production codebase, enabling end-to-end runs on 5-minute video segments. Fixed critical memory issues, boosted Socializing precision to 70%, and automated results reporting to Google Sheets with AI reviewer.

🚀 What is shipped this week?

• Production Codebase Integration: Environment configured and pipeline running inside TimeBack backend on AWS.
• Flexible Segmentation: Switched from 10-second to 5-minute clips; segmentation script updated and validated on multiple videos.
• Socializing Accuracy Boost: Precision improved to 70% from 20% on a newly annotated videos.
• Automated Reporting: Google Sheets integration delivers live AI reviewer metrics and missing-annotation highlights.
• Prompt Strategy Roadmap: Defined plan to unify Distraction & Disengagement detection in a single prompt, with isolated experiments queued.

📈 What’s coming next?

• Execute single-prompt experiment across 2 reference videos for customer review.
• Benchmark clip durations (10s vs 30s vs 1-5 min) against accuracy, latency and cost.
• Flash vs Pro model comparison for production cost optimisation.
• Continue annotation refinement and noisy-environment anti-pattern scoping.

2025-07-18

Summary: Achieved ~80% F1 score on DISTRACTED detection. Developed insight classification with 85.4% accuracy.

🚀 What is shipped this week?

• DISTRACTED Detection Performance:
  • 🎯 F1 Score: 79.15%
  • 📈 Precision: 76.89%
  • 📊 Recall: 81.54%
  • 📹 Duration Tested: 9h 58m 50s
• Insight Classification System:
  • 🧠 85.4% accuracy in distraction insight name classification
  • 🔬 Tested 3 approaches: regex matching, LLM justification, and prompt-level detection
  • ✅ Prompt-level detection approach yielded best results
• AI Reviewer System Enhancement:
  • ⚡ AI Reviewer upgrade merged
  • 📊 Missing annotation detection and review workflow implemented
  • 🎯 Targeting 80%+ precision with improved annotations
  • Manual process of rectifying video became simple

🔬 Technical Insights

• Detection Approach: Got the access of Timeback codebase, it uses single Gemini prompt for 5 anti-patterns with hierarchical logic
• Real-time Processing: 10-second video segments for production deployment
• Annotation Precision: Moving from broad timeframes to exact start/end times

📈 What’s coming next?

• 🎯 Target 80%+ F1 score with improved annotation quality
• 🔧 Direct improvements to production vision processor via pull requests
• 📊 Automated annotation-detection comparison system

This release marks a significant shift from standalone development to direct production integration, establishing a collaborative framework for continuous improvement with the customer’s live system.

2025-07-11

Summary: Achieved strong performance in DISTRACTION detection processor with F1 score of 75.86%. Completed comprehensive audio dependency analysis across 10 videos (proving audio is essential for detection). Implemented robust error handling with real-time notifications and grace period matching for better metric accuracy.

🚀 What is shipped this week?

• DISTRACTION Processor Improvement:
  • 📈 F1 Score: 75.86%
  • 🎯 Precision: 70.83%
  • 📊 Recall: 81.67%
  • 🔬 With 5-second grace period: F1 Score 76.10%
• Audio Dependency Analysis Completed - Detailed Results:
  • 📹 10 videos analyzed for audio vs no-audio performance
  • 🎵 Audio REQUIRED: 28.9% recall drop without audio
  • 🎯 Key finding: Audio essential for “Eyes-Off-Screen”, “Socializing”, “Present but Idle” detection
  • 💡 Precision improves 13.9% without audio, but overall F1 drops 11.1%
• Infrastructure & Quality Improvements:
  • ✅ Merged: Bulk Endpoint Robust Error Handling
  • 🔔 Real-time Error Notifications - Dedicated Google Chat space for pipeline errors
  • ⚡ Grace Period Implementation - 3-5 second tolerance for annotation matching
  • 🤖 AI Reviewer System - Automated review for new Distraction detection processor

🔬 Technical Experiments

• Grace Period Analysis:
  • 📏 3-5 second tolerance: F1 Score 75.86% (optimal)
  • 📏 20 second tolerance: F1 Score 80.46% (too lenient)
  • 📝 Recommendation: 5-second maximum for production use
• Audio Dependency Scoring:
  • 👁️ Eyes-Off-Screen: 0.398 dependency (highest)
  • 🗣️ Socializing: 0.346 dependency (critical)
  • 🪑 Present but Idle: 0.227 dependency (important)

📈 What’s coming next?

• 📝 Annotation quality improvements based on missing events analysis
• 🎯 Target 80%+ precision with corrected annotations
• 📊 Performance optimization for large-scale deployment

2025-07-04

Summary: Achieved major scalability milestone by successfully load testing and deploying 1000-video bulk processing capability. Discovered and documented critical Gemini API rate limit constraints. Optimized pipeline infrastructure with FARGATE and ffmpeg improvements, reducing processing time significantly. Improved DISTRACTIONS detection to 72.27% F1 score on 10+ hours of video content.

🚀 What is shipped this week?

• Load Testing Success - Progressive scaling validated:
  • ✅ 10 videos (45min each): 3 minutes processing time
  • ✅ 50 videos: 10 minutes processing time
  • ✅ 100 videos: 22 minutes processing time
  • ✅ 1000 videos: Successfully submitted via smart batching
• Infrastructure Optimizations:
  • 🔧 FARGATE deployment (converted from EC2)
  • ⚡ Optimized ffmpeg commands for faster video processing
  • 🏗️ Stream copy processing (no re-encoding)
  • 📦 Docker image optimization (80% size reduction by removing heavy librares)

• Google Drive Download Fix - Resolved large file download issues (>185MB) by handling virus scan warning pages

• Comprehensive Scaling Analysis - Processing Scale Analysis with token usage

📊 Performance Metrics

• DISTRACTIONS Detection Results (New Test Harness):
  • 📹 Total Videos: 10 (9h 58m 15s total duration)
  • 🎯 F1 Score: 72.27%
  • 📈 Precision: 65.15%
  • 📊 Recall: 81.14%
  • 📝 Analysis: Good recall, working on precision improvements

📈 Scaling Achievements

• 1000 Student Processing Capability:
  • ⏱️ Expected Processing Time: ~6 hours (including cold starts)
  • 🧮 Token Consumption: 1.1 billion tokens
  • 🎯 Same-day processing capability

🔬 Experiments & Analysis

• Gemini Video Processing Methods - Compared clip-based vs offset-based approaches:
  • ✅ FFmpeg clipping: Recommended (token efficient)
  • ❌ File upload + offset: 2x faster but token inefficient
  • 🚫 Large video limitation: 3+ hour videos fail with token limits

📋 Technical Debt & Issues

• Rate limit calculations need revision based on actual Gemini tiers
• Precision improvement needed for DISTRACTIONS (currently 65.15%)
• Large video processing limitations identified and resolved
• Google Drive download reliability increased

X Post

• Post 1
• Post 2

📈 What’s coming next?

• 🔧 Rate limit optimization strategies (reduce parallel processing)
• 📊 Precision improvement for DISTRACTIONS detection
• 📝 Customer feedback integration on new annotations
• 🚀 Production deployment preparation for 1000+ student scale

2025-06-27

Summary: Documented detailed architecture for the bulk processing pipeline, with each individual Antipattern detection flow. Analyzed the performance of the pipeline across token usage, latency and scalability. Reduced the pipeline latency by 4-5x by optimizing the ffmpeg video processing tasks and parallelization.

What is shipped this week?

• 📃 Detailed Documentation for the Pipeline Flow - Complete flow of the overall pipeline and individual Antipattern detections are documented here: Architecture Flow (This includes all the LLMs and their modalities used as requested by the customer.)
• 📊 Pipeline Analysis - The endpoints now return the token usage and latency metrics for each run. Detailed Analysis of the same is documented here: Pipeline Analysis (This includes antipattern wise token usage, costs and latency as requested by the customer.)

Experiments

• 📽️ Video Processing Optimization - Reduced the pipeline latency by 4-5x by optimizing the ffmpeg video processing tasks and parallelization. Details are here: Video Processing Optimization
• 🧠 Gemini Model Experiments - Experimented feasibility of using Gemini 2.5 Flash to replace Gemini 2.5 Pro wherever possible. Details are here: Gemini Model Experiments

What’s coming next?

• 🧑🏻‍🏫 Work on the new definitions and annotations (of Antipatterns) from the customer.
• 🚀 Optimizing and Scaling the pipeline for ~1000 students with 3 hours of video per student per day.

2025-06-20

Summary: Successfully deployed bulk processing endpoints with comprehensive performance metrics tracking. Infrastructure scaled to support 50 concurrent jobs. Strategic pivot based on customer feedback - pausing Test Harness experiments to await new antipattern definitions while focusing on cost analysis and scalability assessment for potential 1,000 student deployment.

🚀 What is shipped this week?

• Bulk Processing API fully operational - Handles single and bulk video processing with live Swagger documentation at this
• Infrastructure upgrade to 50 concurrent jobs
• 4 Major PRs merged:
  • Socializing Improvement
  • AI Reviewer System
  • Distractions Detection
  • Bulk Endpoint

• Comprehensive API Documentation - Live Swagger Docs with updated I/O schema

• Strategic status:
  • ✅ Bulk endpoints deployed and tested
  • ✅ Performance metrics integrated
  • 🔄 Awaiting new antipattern definitions from customer
  • 📊 Cost, Latency and scalability analysis in progress

📈 What’s coming next?

• Cost analysis documentation with per-antipattern breakdown
• Flash vs Pro model comparison for efficiency
• Scalability analysis for 1,000 students
• Processing time optimization strategies

2025-06-13

Summary: Achieved 70% F1 score on IDLING detector through systematic analysis and architectural improvements. Completed comprehensive technical architecture design with 4 ITDs. Created architecture for production-ready bulk processing API with full documentation and customer integration materials.

🚀 What is shipped this week?

• IDLING detector improved to 70% F1 score - Analyzed 7 test harness videos (1h+ total duration) and achieved significant performance improvement
• 4 Technical Architecture Decisions (ITDs) documented and finalized:
  • Processing Engine: AWS Batch vs Lambda
  • Queue Strategy: Batch Native vs SQS
  • Parallel Processing: Multi-level Strategy
  • Output Format: Unified JSON Schema
• Production-ready Bulk Processing API with documentation and testing framework
• Hosted API Documentation - Live Swagger Docs
• 
• Delivered Customer Required Documentations - I/O Interface specification and Progress Tracker

📊 Where have we reached?

• Current performance scores:

Antipattern	F1 Score Status
Away From Seat	✅ 92.00%
Idling (No webcam)	✅ 92.62%
Non Learning Content	☑️ 85.00%
Idling (Webcam)	📈 70.07%
Socializing	🕛 60.00%

📋 Research & Recommendations

• IDLING Detection Simplification - Research-backed 15-second threshold and binary classification approach
• Performance Analysis - 7 videos analyzed (avg 8m 39s duration) with detailed metrics and edge case identification

📈 What’s coming next?

• Implementation of AWS Batch-based processing pipeline
• Further improvement of SOCIALIZING detector accuracy
• Making new definition of DISTRACTION and INEFFICIENT_LEARNING
• Testing new bulk API

2025-06-06

Summary: Implemented and Achieved 85% score on NON_LEARNING_CONTENT antipattern. Resumed work on SOCIALIZING antipattern, current F1 score is 53%. Created an Analysis Dashboard to quickly analyze the results of the detectors and iterate on them.

🚀 What is shipped this week?

• Implemented NON_LEARNING_CONTENT antipattern. Reached 85% F1 score with 90%+ Precision. Helps detect when student watching or engaging in non-learning activities during study sessions.
• Resumed work on SOCIALIZING antipattern, current F1 score is 53%. Detects when student is socializing with others during study sessions.
• Improved IDLING detector to 70% F1 score.
• Created an Analysis Dashboard to quickly analyze the results of the detectors identifies issues with them to iterate faster.
• 

📊 Where have we reached?

• Current score are:

Antipattern	Aggregate F1 Score (from Test harness)
Away From Seat	✅ 92.00 %
Idling (No webcam)	✅ 92.62 % (on 8 videos)
Non Learning Content	☑️ 85.00 %
Idling (Webcam)	📈 70.07 %
Socializing	🕛 53.00 %

📢 Build in Public

• Vikash’s Post: Frame Similarity
• Kabir’s Post: Prompt Fine Tuning

📈 What’s coming next?

• Improved SOCIALIZING and IDLING detectors.
• Integration with TimeBack platform.

2025-05-30

Summary: Parked SOCIALIZING for now, awaiting customer clarification. Improved scores for AWAY_FROM_SEAT to 92%. Started on IDLING and IDLING_NO_WEBCAM.

🚀 What is shipped this week?

• Improved AWAY_FROM_SEAT detector to 92% F1 score. (Tested pipeline on 4H+ of video)
• Started on IDLING and IDLING_NO_WEBCAM detectors.
  • IDLING detector reached 61.9% F1 scores.
  • IDLING_NO_WEBCAM detector reached 92.62% F1 scores.
• Implementation details documented here: Implementation Details

📊 Where have we reached?

• Current score are:

Antipattern	Aggregate F1 Score (from Test harness)
Away From Seat	✅ 92.00 %
Idling (No webcam)	✅ 92.62 % (on 8 videos)
Idling (Webcam)	📈 61.90 %
Socializing (while active)	🕛 28.64 % (paused, awaiting clarification)
Socializing (while inactive)	🕛 50.98 % (paused, awaiting clarification)

📈 What’s coming next?

• Add support for NON_LEARNING_CONTENT antipattern.
• Improve Accuracy for IDLING antipattern.
• Improve Accuracy for SOCIALIZING antipattern post definition change in Test Harness.

2025-05-23

Summary: New pipeline architecture deployed and running. Endpoints working. SOCIALIZING and AWAY_FROM_SEAT implemented. IDLING WIP.

🚀 What is shipped this week?

• New Pipeline Architecture (capable of handling bulk requests) fully implemented and deployed to AWS. (REPO: Timeback-AP)
• An Endpoint to get the anti-pattern report for a given video link (student study session).
  • Endpoint: https://zsf3ynhjmk.execute-api.us-east-1.amazonaws.com/dev/jobs
  • Detailed steps to use the API can be found here: README
• We’ll be sharing a demo on how to use this endpoint soon.
• SOCIALIZING and AWAY_FROM_SEAT antipattern detectors are implemented.

📊 Where have we reached?

• There are peculiar edge cases and clarifications in definition of some anti-patterns that we are discussing with TestHarness team.
• Current score with the new pipeline are:

Antipattern	Aggregate F1 Score (from Test harness)
Socializing (while active)	28.64 %
Socializing (while inactive)	50.98 %
Away From Seat	72.82 %

X-Post & Demo

• Post 1
• Post 2
• Demo

📈 What’s coming next?

• Add support for more anti-patterns (IDLING and NON_LEARNING_CONTENT).
• Improve Accuracy for SOCIALIZING and AWAY_FROM_SEAT.

2025-05-16

Summary: Shifted from a realtime pipeline to a scalable postprocessing pipeline for processing sessions in bulk.

🛞 New Scalable PostProcessing Pipeline

• Our earlier pipeline was made to work in a realtime setting detecting what was going on the screen in realtime.
• This was good to run and see, but posed significant limitations on being able to process already recorded student sessions, process such sessions in bulk and test them with test harness (set of pre-defined manually annotated session videos).
• The goal of the project has now shifted to being able to generate accurate reports for already recorded student sessions instead of giving realtime alerts.
• So we decided to revamp our pipeline from scratch to a scalable postprocessing pipeline.

🔄 Pipeline Status

• The new postprocessing pipeline is architectured for AWS and initial implementation is in place. (PIPELINE)
• First antipattern (SOCIALIZING) is implemented in the new framework.
• We now have an endpoint where we can pass a video link and get the anti-pattern report for it. (currently only for socializing)
• Posting a demo soon for the same.

➡️ Next Steps

• Deploy the pipeline to AWS and make it ready for production.
• Implement the testing for test harness framework.

2025-05-09

Summary: Implemented the framework to test all 4 high priority antipatterns with test harness and got initial numbers.

🚧 Testing

• Implemented getting the time intervals for each anti-pattern from each detector.
• Used that even json to run test harness and get F1 scores.

Antipattern	Average F1 Score (from Test harness)
Socializing	65.8 %
Non Learning Content	20.8 %

• Resolving errors with other 2 anti-patterns before we can get results for them.
• Identified issues leading to low F1 scores: Initial Results and Issues Identified

➡️ Next steps:

• Systematically make improvements to increase these numbers targetting 90 % score.
• Identify and handle edge cases (like no camera feed, black screens etc.)

---

2025-05-05

Summary: Implemented the initial framework for testing Anti-Pattern detectors with Test Harness

🚧 Testing

• Test Harness - The tool provided by TimeBack team to test the anti-pattern detectors with a set of pre-defined manually annotated test cases (videos of student studying sessions).
• Implemented the initial setup to store our various anti-pattern detector results in a unified format with events and their start and end times.
• Implemented the initial setup to parse our event jsons as Caliper events (input expected by TimeBack platform) and run Test Harness to get results. (True positives, False Positives, True Negatives, False Negatives, Precision, Recall, F1 Score)
• Tested first set of results for socializing detector, results seem meaningless because of synchronization issues between our session timestamps and test harness timestamps.
• Added the concept of inertia in anti-pattern detection. Instead of detecting anti-patterns on a per-instance basis, they are now detected on a range basis. This introduces the concept of inertia for anti-patterns like socializing, providing more consistent and meaningful detection results aligned with the test harness.

➡️ Next steps:

• Fix synchronization issue to get meaningful results that’ll allow us to evaluate and compare the effectiveness of Anti-Pattern detectors.
• Extend testing framework across all detectors and get results for them.
• Unify all individual detectors.