An adaptive system for optimizing continuous integration and continuous delivery (CI / CD) using dynamic generation of parallel build graphs and runtime dependency resolution.

Abstract

An adaptive optimization system (100) for continuous integration / continuous delivery (CI / CD) that utilizes the dynamic generation of parallel build graphs and the resolution of runtime dependencies, comprising: a change detection module configured to continuously monitor one or more version control systems and automatically detect changes to the source code, configuration files, build scripts, or related artifacts in real time or near real time; a dependency analysis module that is functionally coupled with the change detection module, wherein the dependency analysis module is configured to identify and resolve both static and runtime dependencies between a variety of software components, services, and tasks by analyzing source code structures, metadata, execution environments, and configuration parameters; a dynamic graph generation module configured to create a directed acyclic execution graph for each pipeline instance based on identified dependencies, where the graph includes a variety of nodes representing executable tasks, including build, test, integration, validation, and deployment operations, and a variety of edges representing runtime-determined dependency relationships between the tasks; a scheduling engine configured to process the directed acyclic execution graph and dynamically determine an optimized execution plan by identifying independent and interdependent tasks, enabling fine-grained parallel execution of tasks by resolving dependency constraints at runtime, and distributing said tasks across available computational resources based on one or more parameters, including resource availability, execution priority, historical execution data, and predicted task duration; a distributed execution framework configured to execute scheduled tasks across one or more heterogeneous computing environments, including cloud-based systems, virtualized infrastructure, and local servers, wherein the distributed execution framework further provides dynamic load balancing, fault tolerance through automatic task retry and reassignment, and result caching to avoid redundant computations; a learning and optimization module configured to capture, store, and analyze historical pipeline execution data, including task execution times, resource utilization patterns, error events, and dependency resolution results, and further configured to apply machine learning or statistical modeling techniques to continuously refine pipeline planning decisions, improve prediction accuracy, and optimize subsequent pipeline executions; wherein the system (100) is further configured to perform incremental pipeline execution by selectively executing only those tasks affected by the detected changes and their dependent tasks, thereby reducing redundant processing steps, minimizing the overall execution time and improving computational efficiency; where the system (100) enables adaptive, scalable and efficient CI / CD pipeline orchestration through real-time dependency resolution and dynamic generation of parallel build graphs.

Description

[0001] The present invention relates to software development, in particular the optimization of CI / CD pipelines using dynamic graph generation, dependency resolution and parallel execution in distributed computing environments.

[0002] Continuous integration and continuous deployment systems are essential components of modern software development, enabling the automated building, testing, and deployment of applications. Traditional CI / CD pipelines are typically defined using static workflows, where tasks are pre-ordered in fixed sequences or limited parallel phases. While such approaches offer predictability, they do not utilize computing resources efficiently, especially in large projects with complex dependencies between components. Existing systems often suffer from redundant builds, inefficient task serialization, and an inability to adapt to incremental code changes. Static pipelines do not account for real-time changes in dependencies, resulting in the unnecessary execution of irrelevant modules.Furthermore, current parallelization strategies are coarse-grained and do not dynamically adapt to the workload or system performance.

[0003] To solve this problem, the present invention offers an adaptive optimization system for Continuous Integration / Continuous Deployment (CI / CD) that utilizes the dynamic generation of parallel build graphs and the resolution of dependencies at runtime.

[0004] The system dynamically generates execution graphs based on real-time code changes and dependency analysis, significantly reducing unnecessary task executions and improving overall pipeline efficiency. This enables faster feedback cycles for developers and minimizes redundant computational overhead in large codebases.

[0005] The system is able to enable fine-grained parallelism through runtime dependency resolution, identifying independent build and test tasks and executing them simultaneously across distributed computing environments, thereby improving resource utilization and reducing overall pipeline execution time compared to traditional static workflows.

[0006] The system features a feedback-driven learning mechanism that continuously analyzes historical pipeline performance data to refine planning strategies, predict bottlenecks, and dynamically optimize task allocation, leading to a gradual improvement in system performance and intelligent decision-making over time.

[0007] The system ensures high scalability and fault tolerance by distributing tasks across multiple computing nodes and dynamically reassigning failed or delayed tasks, thereby ensuring robustness, reliability, and consistent performance in large-scale and heavily loaded operating environments.

[0008] The system enables seamless integration into existing CI / CD tools and infrastructures, allowing companies to implement the system without major architectural changes and thus benefit from improved automation, increased efficiency and significantly reduced deployment times.

[0009] The present invention discloses an adaptive CI / CD optimization system that utilizes the dynamic generation of parallel build graphs in combination with runtime dependency resolution. The system monitors code repositories for changes and creates a dependency-aware execution graph in real time. Each node in the graph represents a build, test, or deployment task, while edges represent dependencies that are dynamically identified through static and runtime analysis. A scheduling engine evaluates the graph and executes tasks in parallel wherever dependencies allow, thereby optimizing resource allocation across distributed environments. The system further integrates a machine learning module that analyzes historical pipeline runs to improve the predictive accuracy of task runtimes and optimal execution paths.The invention supports incremental builds, eliminates redundant calculations, and adapts to fluctuating workloads. It increases pipeline efficiency, reduces execution time, and improves scalability, making it suitable for modern DevOps environments with complex software architectures.

[0010] Fig. illustrates an adaptive CI / CD pipeline optimization system based on the dynamic generation of parallel build graphs and the resolution of runtime dependencies.

[0011] Fig.Figure 100 illustrates an adaptive system for optimizing CI / CD pipelines, based on the dynamic creation of a parallel build graph and the resolution of runtime dependencies. The system (100) is divided into a pipeline preparation layer and an execution and optimization layer, where a change detection module monitors version control systems to identify changes in the source code and associated artifacts, a dependency analysis module identifies static and runtime dependencies between tasks, and a dynamic graph generation module creates a directed acyclic execution graph (DAG) that represents the relationships between the tasks.The DAG is processed by a scheduling engine to identify parallelizable tasks and generate an optimized execution plan, which is executed by a distributed execution framework across heterogeneous computing environments with load balancing and fault tolerance. Meanwhile, the execution results are analyzed by a learning and optimization module to refine future scheduling decisions through a feedback loop, enabling incremental execution, reducing redundant processing, and improving the overall efficiency and scalability of the pipeline.

[0012] The present invention provides a system (100) and a method for optimizing CI / CD pipelines through dynamic graph generation and runtime dependency resolution. The system (100) comprises a change detection module, a dependency analyzer, a graph generator, a scheduling engine, a distributed execution framework, and a learning module. The change detection module monitors version control systems and identifies modified files, commits, or branches. Following detection, the dependency analyzer evaluates both static code dependencies and runtime configuration dependencies to determine relationships between components. This analysis includes parsing build scripts, configuration files, and metadata to create a comprehensive dependency map.The graph generator transforms the dependency map into a directed acyclic graph, where nodes represent executable tasks such as compiling, testing, packaging, and deployment. Edges represent dependencies that define the execution order. Unlike static pipelines, this graph is dynamically generated with each pipeline execution based on actual changes and system state. The scheduling engine processes the graph and identifies tasks that can be executed in parallel. It allocates these tasks to available computing resources in a distributed environment, such as cloud-based containers or virtual machines. The scheduler considers factors such as task priority, estimated execution time, and resource availability to optimize performance.The distributed execution framework runs tasks across multiple nodes, ensuring load balancing and fault tolerance. If a task fails, the system (100) automatically retryes or reassigns the task to another node. Intermediate results are cached to avoid redundant calculations in subsequent iterations. The learning module collects historical data from pipeline executions, including task duration, failure rates, and resource utilization. Using this data, it applies predictive models to improve scheduling decisions, estimate task completion times, and identify potential bottlenecks. In an experimental setup, the system (100) was tested on a large-scale microservices project with 500 modules.Compared to a conventional static pipeline, the proposed system (100) reduced the average build time by 45% and improved resource utilization by 30%. Parallel execution efficiency increased significantly due to the finely tuned dependency resolution. Furthermore, the system (100) demonstrated high fault tolerance under heavy load, maintaining consistent performance with minimal downtime. The invention can be implemented using container orchestration platforms and integrated into existing CI / CD tools such as Jenkins, GitLab CI, or GitHub Actions. It supports multiple programming languages ​​and build systems, making it versatile for use in various development environments.

Claims

[1] An adaptive optimization system (100) for continuous integration / continuous delivery (CI / CD) that utilizes the dynamic generation of parallel build graphs and the resolution of runtime dependencies, comprising: a change detection module configured to continuously monitor one or more version control systems and automatically detect changes to the source code, configuration files, build scripts, or related artifacts in real time or near real time; a dependency analysis module that is functionally coupled with the change detection module, wherein the dependency analysis module is configured to identify and resolve both static and runtime dependencies between a variety of software components, services, and tasks by analyzing source code structures, metadata, execution environments, and configuration parameters; a dynamic graph generation module configured to create a directed acyclic execution graph for each pipeline instance based on identified dependencies, where the graph includes a variety of nodes representing executable tasks, including build, test, integration, validation, and deployment operations, and a variety of edges representing runtime-determined dependency relationships between the tasks; a scheduling engine configured to process the directed acyclic execution graph and dynamically determine an optimized execution plan by identifying independent and interdependent tasks, enabling fine-grained parallel execution of tasks by resolving dependency constraints at runtime, and distributing said tasks across available computational resources based on one or more parameters, including resource availability, execution priority, historical execution data, and predicted task duration; a distributed execution framework configured to execute scheduled tasks across one or more heterogeneous computing environments, including cloud-based systems, virtualized infrastructure, and local servers, wherein the distributed execution framework further provides dynamic load balancing, fault tolerance through automatic task retry and reassignment, and result caching to avoid redundant computations; a learning and optimization module configured to capture, store, and analyze historical pipeline execution data, including task execution times, resource utilization patterns, error events, and dependency resolution results, and further configured to apply machine learning or statistical modeling techniques to continuously refine pipeline planning decisions, improve prediction accuracy, and optimize subsequent pipeline executions; wherein the system (100) is further configured to perform incremental pipeline execution by selectively executing only those tasks affected by the detected changes and their dependent tasks, thereby reducing redundant processing steps, minimizing the overall execution time and improving computational efficiency; where the system (100) enables adaptive, scalable and efficient CI / CD pipeline orchestration through real-time dependency resolution and dynamic generation of parallel build graphs. [2] System (100) according to claim 1, wherein the change detection module is further configured to detect incremental changes at the file, function or module level and to trigger selective pipeline execution based on the detected scope of change. [3] System (100) according to claim 1, wherein the dependency analysis module is further configured to use static code parsing, runtime instrumentation and configuration mapping techniques to generate an updated and context-aware dependency record for each pipeline execution. [4] System (100) according to claim 1, wherein the dynamic graph generation module is further configured to modify the directed acyclic graph during execution in real time to accommodate dynamic dependencies, conditional task execution paths and variations in the runtime environment. [5] System (100) according to claim 1, wherein the scheduling engine is further configured to prioritize tasks based on critical path analysis, historical execution patterns and predefined policy rules to optimize the overall throughput and latency of the pipeline. [6] System (100) according to claim 1, wherein the planning engine is further configured to dynamically scale the allocation of computing resources by communicating with cloud orchestration platforms and container management systems. [7] System (100) according to claim 1, wherein the distributed execution framework is further configured to implement checkpointing and state maintenance mechanisms to resume interrupted tasks without re-executing operations that have already been completed. [8] System (100) according to claim 1, wherein the distributed execution framework is further configured to manage a cache of intermediate artifacts and reuse these artifacts across multiple pipeline executions to reduce redundant computations and improve efficiency.

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