Description:FIELD OF INVENTION
The present invention relates to information technology and application development. More specifically relates to a system and method for establishing evidence-bound productivity baselines in application development using generative artificial intelligence and statistical verification.
BACKGROUND
An evidence-bound productivity baseline in application development establishes a benchmark for team performance using objective data, enabling better project management, identifying productivity gaps, improving process efficiency, supporting decision-making, and guiding team improvement initiatives. It helps teams monitor progress, measure the effectiveness of changes, and set realistic goals by comparing actual performance against data-driven benchmarks.
Modern software development organizations rely on a heterogeneous ecosystem of tools such as Jira, GitHub/GitLab, Aha, Confluence, build pipelines, and incident tracking systems. Each system provides partial, inconsistent, and often erroneous data regarding the productivity of individuals and teams. Conventional productivity measurement methods assume consistent updates and rely heavily on declared metrics (e.g., story points, time logs). These are prone to; missing updates (tickets never moved to “Done”), incorrect updates (effort under/over-stated), and disconnected artifacts (PRs merged without linked issues). As a result, organizations cannot reliably establish baselines for throughput, velocity, or efficiency, leading to misallocation of resources, poor forecasting, and multi-million-dollar losses in planning.
PRIOR ARTS:
2112/DEL/2015 discloses a A method and apparatus can be configured to receive a data set of values relating to a process. The data set of values correspond to values measured while the process is performed over a duration of time. The method also includes performing first statistical calculations on a first data subset of values. The values of the first data subset is a subset of the entire received data set of values. The values of the first data subset of values correspond to values that are of a first timeframe of the duration of time. The method also includes displaying first calculated results of the first statistical calculations. The method also includes determining whether performing the process has crossed a first threshold baseline. The first threshold baseline is based on the first statistical calculations. The method also includes transmitting a first alert to a user if the process is determined to have crossed the first threshold baseline.

US11948003B2 discloses a data processing method and system for automated construction, resource provisioning, data processing, feature generation, architecture selection, pipeline configuration, hyperparameter optimization, evaluation, execution, production, and deployment of machine learning models in an artificial intelligence solution development lifecycle. In accordance with various embodiments, a graphical user interface of an end user application is configured to provide a pre-configured template comprises an automated ML framework for data import, data preparation, data transformation, feature generation, algorithms selection, hyperparameters tuning, models training, evaluation, interpretation, and deployment to an end user.

The prior art talks about the statistical calculations (to yield the calculated results) on the received data set of measurements in accordance to a schedule, and disclosure relates to the field of artificial intelligence type computers and digital data processing systems and corresponding data processing methods and products for emulation of intelligence, machine learning systems, and artificial neural networks. However, there is a need for a novel system that combines data normalization, statistical modeling, cross-system graph linking, and generative AI verification against historical and external corpora to produce reliable, evidence-bound productivity baselines.

DEFINITIONS
The expression “system” used hereinafter in this specification refers to an ecosystem comprising, but not limited to, system for automatically defining post-deployment success metrics with input and output devices, processing unit, plurality of mobile devices, a mobile device-based application. It is extended to computing systems like mobile phones, laptops, computers, PCs, and other digital computing devices.
The term “input unit” used hereinafter in this specification refers to, but is not limited to, mobile, laptops, computers, PCs, keyboards, mouse, pen drives or drives.
The term “processing unit” refers to the computational hardware or software that performs the data base analysis, generation of graphs, detection of dead code, processing, removal of dead code, and like. It includes servers, CPUs, GPUs, or cloud-based systems that handle intensive computations.
The term “output unit” used hereinafter in this specification refers to hardware or digital tools that present processed information to users including, but not limited to computer monitors, mobile screens, printers, or online dashboards.
The term “LLM” or “Large Language Model” used hereinafter in this specification refers to a type of artificial intelligence that uses deep learning algorithms and massive amounts of text data to understand, process, and generate human-like text for various tasks, including translation, summarization, and content creation.
The term “JSON” or “JavaScript Object Notation” used hereinafter in this specification refers to a lightweight, text-based open standard data interchange format designed for human-readable data interchange.
OBJECTS OF THE INVENTION
The primary object of the present invention is to provide a system and method for establishing evidence-bound productivity baselines in application development using generative and statistical verification.
Another object of the invention is to provide a statistical forecaster that computes a plausible effort interval for each task.
Yet another object of the invention is to provide a generative verification module that critiques reported effort by comparing the task against the internal historical data and curated external corpora.
Yet another object of the invention is to provide the LLM that outputs a structured JSON verdict with citations to nearest-neighbor tasks and metrics.
Yet another object of the invention is to provide an evidence-bound consistency score that computes from statistical hit, LLM verdict and bookkeeping integrity.
Yet another object of the invention is to provide a baseline construction that yields defensible productivity vectors for teams/projects/sprints, with confidence intervals.
SUMMARY
Before the present invention is described, it is to be understood that the present invention is not limited to specific methodologies and materials described, as these may vary as per the person skilled in the art. It is also to be understood that the terminology used in the description is for the purpose of describing the particular embodiments only and is not intended to limit the scope of the present invention.
The present invention describes a system and method for establishing evidence-bound productivity baselines in software development organizations, even when underlying software development lifecycle (SDLC) systems are inconsistent or incomplete. The invention ingests heterogeneous data from issue trackers, source control, documentation, and release systems, and normalizes them into a unified schema. A probabilistic entity resolution engine constructs a knowledge graph linking tickets, commits, pull requests, builds, tests, and documentation. A statistical forecaster, comprising machine learning regressors, hierarchical Bayesian adjusters, and conformal predictors, generates a plausible interval of effort for each work item. A generative AI module, constrained to evidence from internal history and curated external repositories, verifies reported efforts by critiquing them against retrieved reference tasks, producing structured verdicts with citations. A consistency scoring engine fuses statistical plausibility, AI verdicts, and bookkeeping integrity into a unified measure of reliability. The system outputs productivity baseline vectors with confidence intervals for teams, projects, and sprints, enabling enterprises to detect false or missing updates, improve resource allocation, and achieve significant return on investment through more accurate planning and benchmarking.
According to an aspect of the present invention, the statistical forecaster (ensemble of gradient boosted trees, hierarchical Bayesian models, and conformal predictors) computes a plausible effort interval for each task. The generative AI verification module then critiques reported effort by comparing the task against: Internal historical data (reference-class forecasting), and Curated external corpora (ranked GitHub repositories). The LLM outputs a structured JSON verdict (Reasonable / Understated / Overstated / Inconclusive), with citations to nearest-neighbor tasks and metrics. An evidence-bound consistency score is computed from statistical hit, LLM verdict, and bookkeeping integrity. This score feeds into baseline construction, yielding defensible productivity vectors for teams/projects/sprints, with confidence intervals.
According to an aspect of the present invention, the system yields high ROI by detecting missing or false updates automatically, producing reliable baselines even under noisy data, enabling objective benchmarking across teams, and improving resource allocation and project forecasting.
BRIEF DESCRIPTION OF DRAWINGS
A complete understanding of the present invention may be made by reference to the following detailed description which is to be taken in conjugation with the accompanying drawing. The accompanying drawing, which is incorporated into and constitutes a part of the specification, illustrates one or more embodiments of the present invention and, together with the detailed description, it serves to explain the principles and implementations of the invention.
FIG. 1 is a block diagram of the system architecture of the present invention.
FIG. 2 is the workflow of the method of the present invention.
DETAILED DESCRIPTION OF INVENTION:
Before the present invention is described, it is to be understood that this invention is not limited to methodologies described, as these may vary as per the person skilled in the art. It is also to be understood that the terminology used in the description is for the purpose of describing the particular embodiments only and is not intended to limit the scope of the present invention. Throughout this specification, the word “comprise”, or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps. The use of the expression “at least” or “at least one” suggests the use of one or more elements or ingredients or quantities, as the use may be in the embodiment of the invention to achieve one or more of the desired objects or results. Various embodiments of the present invention are described below. It is, however, noted that the present invention is not limited to these embodiments, but rather the intention is that modifications that are apparent are also included.
The present invention relates generally to software engineering analytics, and more specifically to systems and methods for constructing productivity baselines from inconsistent software development lifecycle (SDLC) systems by applying statistical modeling, graph-based entity linking, and generative AI for verification and correction of reported efforts. The invention provides a computer-implemented system that ingests data from multiple SDLC sources, normalizes them into a unified schema, and constructs a knowledge graph linking work items, commits, PRs, tests, and documentation.
According to the embodiment of the present invention, as described in FIG. 1, the system architecture comprises of an input unit , a processing unit and output unit , wherein the processing unit further comprises of ingestion layer module, entity resolution engine module, knowledge graph store module, feature extractor module, reference corpus manager module, statistical forecaster module, generative AI critic module, consistency scorer module, baseline generator module and human feedback loop module.
According to the embodiment of the present invention, the Ingestion Layer module provides connectors for Jira, Git, CI/CD, and documentation sources, where events are normalized into a unified Fact Event schema with time-aligned attributes. The entity Resolution Engine module applies probabilistic record linkage and embedding similarity to connect issues, pull requests, and documents, which are then stored in the Knowledge Graph Store module that maintains entities and typed edges. The Feature Extractor module computes patch-level metrics such as LOCΔ, churn, and complexity, along with process metrics like review latency and CI health, as well as historical priors including bug density and team norms. The Reference Corpus Manager module curates internal and external high-quality repositories, ranking them by linkage density, CI health, and release cadence. Forecasting is handled by the Statistical Forecaster, module which employs an ensemble of LightGBM regressors, hierarchical Bayesian adjusters, and conformal predictors to output effort intervals. The Generative AI Critic module leverages a large language model constrained to evidence-bound reasoning, producing structured JSON verdicts referencing retrieved neighbor. These outputs are integrated through the Consistency Scorer module, which combines statistical hit, AI verdict, and an integrity index into a unified score. The Baseline Generator module aggregates consistency-weighted metrics across teams and sprints to derive a productivity baseline vector with confidence intervals, while the Human Feedback Loop module enables triage, active learning, and iterative retraining of linkage models, effort predictors, and LLM prompt exemplars.
According to the embodiment of the present invention, the computer-implemented system transforms inconsistent and incomplete software development lifecycle (SDLC) data into a reliable productivity baseline through a combination of statistical modeling, graph-based linking, and generative AI verification.
1. Data Ingestion and Normalization: The system first ingests events from multiple heterogeneous SDLC sources, including but not limited to Jira (issues, epics, tasks), Git (commits, pull requests), Aha! (product features), CI/CD pipelines (build and deployment records), and documentation repositories. Each event is normalized into a common fact-event schema, comprising a source identifier, entity type, verb (e.g., “created,” “merged,” “closed”), timestamp, and associated attributes.

2. Entity Resolution and Knowledge Graph Construction: Because SDLC systems are often poorly linked or inconsistently updated, the system performs probabilistic record linkage. A matching engine evaluates similarity features including textual similarity (embedding-based), identifier overlap (e.g., Jira ticket IDs in commit messages), file footprint overlap, author/reviewer intersection, and temporal proximity. Each entity (e.g., issue, commit, PR, doc) is stored as a node in a knowledge graph, with edges representing semantic or inferred relationships such as “implements,” “references,” or “depends on.” When confidence is low, edges are flagged as imputed with associated probabilities.

3. Feature Extraction and Reference Corpus: For each work item, the system extracts deterministic metrics such as lines of code changed, number of files modified, code churn, test deltas, reviewer count, review latency, build duration, and dependency fan-in/out. These are supplemented by semantic features derived from embeddings of task descriptions and code diffs. In parallel, the system maintains a reference corpus of historical tasks from both internal history and curated external repositories (e.g., high-quality GitHub projects ranked by linkage density, CI health, and release cadence). This corpus provides reference classes against which new work items can be compared.

4. Statistical Effort Prediction: A hybrid forecasting model combines:
• A gradient boosted decision tree regressor trained on task features,
• A hierarchical Bayesian adjuster to account for local team and component norms, and
• A conformal prediction layer that produces an effort interval [L, U] with guaranteed statistical coverage.
This interval represents the system’s evidence-based prediction of plausible effort for a given work item.
5. Generative AI Verification: To detect missing or incorrect updates, a generative AI module is invoked. This module receives:
• The normalized task description,
• Extracted features,
• The predicted interval [L, U],
• The reported effort from the SDLC system, and
• Top-k nearest neighbors retrieved from the reference corpus.
The AI is instructed to return a structured JSON verdict, classifying the reported effort as Reasonable, Understated, Overstated, or Inconclusive. The output must include rationale bullets, citations to neighbor tasks, identified risk factors (e.g., database migration, cross-team dependencies), and suggested corrections.
6. Consistency Scoring: The system computes a Consistency Score that integrates:
• Statistical plausibility (whether the reported effort falls inside the conformal interval),
• The AI verdict with confidence, and
• A bookkeeping integrity index based on completeness of links, status consistency, and presence of CI/review evidence.
The resulting score provides a quantitative measure of how trustworthy each reported effort is.
7. Productivity Baseline Generation: Work items are aggregated across teams, projects, and sprints. Using consistency-weighted averages and bootstrap confidence intervals, the system produces a productivity baseline vector containing throughput, cycle times, review efficiency, and defect proxies. These baselines are robust to missing updates and can be compared longitudinally across time periods or between teams.

8. Human-in-the-Loop Refinement: For flagged work items with low consistency scores, the system surfaces a triage interface presenting patch summaries, statistical intervals, and AI rationales. Human reviewers can quickly identify true bookkeeping errors, estimation misses, or scope creep. Reviewer feedback is logged and used to improve linkage models, statistical predictors, and AI prompts through active learning.
According to the embodiment of the present invention, as described in FIG.2, the method for establishing evidence-bound productivity baselines in application development using generative artificial intelligence and statistical verification, comprising the steps of
• ingesting events from multiple heterogeneous systems;
• normalizing events into a unified schema;
• constructing a knowledge graph of work items and artifacts;
• predicting a plausible effort interval using a hybrid statistical forecaster;
• verifying reported effort using a generative AI module constrained to evidence from historical and external corpora;
• generating a consistency score for each work item; and
• producing a productivity baseline vector with confidence intervals.
According to the embodiment of the present invention, the Edge Case Handling is addressed through specific mechanisms to ensure robustness and accuracy. For missing updates, PR merges along with test changes are used to impute completion, while wrong updates are detected by cross-validating against the inferred state trajectory using an HMM. In the case of mass refactors, these are classified separately and compared to a designated refactor reference class, whereas docs-only tasks are validated through review density and neighbouring document tasks. Finally, for cross-team tasks, an additional penalty is applied to account for coordination overhead.
, Claims:We claim,
1. A system and method for establishing evidence-bound productivity baselines in application development
characterized in that
the system constructs productivity baselines from inconsistent software development lifecycle systems by applying statistical modeling, graph-based entity linking, and generative AI for verification and correction of reported efforts;
the system architecture comprises of an input unit , a processing unit and output unit , wherein the processing unit further comprises of ingestion layer module, entity resolution engine module, knowledge graph store module, feature extractor module, reference corpus manager module, statistical forecaster module, generative AI critic module, consistency scorer module, baseline generator module and human feedback loop module;
the method for establishing evidence-bound productivity baselines in application development, comprises the steps of
• ingesting events from multiple heterogeneous systems;
• normalizing events into a unified schema;
• constructing a knowledge graph of work items and artifacts;
• predicting a plausible effort interval using a hybrid statistical forecaster;
• verifying reported effort using a generative AI module constrained to evidence from historical and external corpora;
• generating a consistency score for each work item; and
• producing a productivity baseline vector with confidence intervals.

2. The system and method as claimed in claim 1, wherein the generative AI module is constrained to structured JSON output with mandatory citations to retrieved neighbors and metrics.
3. The system and method as claimed in claim 1, wherein the generative AI verification module critiques reported effort by comparing the task against Internal historical data by reference-class forecasting, and Curated external corpora by ranked GitHub repositories.

4. The system and method as claimed in claim 1, wherein the statistical forecaster comprises at least one of a gradient boosted decision tree, hierarchical Bayesian regression, and conformal prediction.

5. The system and method as claimed in claim 1, wherein external corpora are curated and ranked based on linkage density, CI health, release cadence, and contributor stability.

6. The system and method as claimed in claim 1, wherein missing or incorrect updates are inferred using a hidden Markov model over workflow states and observed events.

7. The system and method as claimed in claim 1, wherein the consistency score combines conformal interval hit, generative AI verdict, and bookkeeping integrity index.

8. The system and method as claimed in claim 1, wherein the consistency score feeds into baseline construction, yielding defensible productivity vectors for teams, projects, and sprints, with confidence intervals.

9. The system and method as claimed in claim 1, wherein the system yields high ROI by detecting missing or false updates automatically, producing reliable baselines even under noisy data, enabling objective benchmarking across teams, and improving resource allocation and project forecasting.

10. The system and method as claimed in claim 1, wherein for missing updates, PR merges along with test changes are used to impute completion, while wrong updates are detected by cross-validating against the inferred state trajectory using an HMM, in the case of mass refactors, these are classified separately and compared to a designated refactor reference class, whereas docs-only tasks are validated through review density and neighboring document tasks and for cross-team tasks, an additional penalty is applied to account for coordination overhead.