Software Composition Analysis Market Size, Share, Growth Analysis, By Component(Solution (Capabilities (Vulnerability Detection, Risk Management, Alerting, And Reporting), By Deployment Type(On-Premises and Cloud), By Organization Size(Large Enterprises, Small and Medium-Sized Enterprises), By Vertical(BFSI, Government and Defense, Telecom and IT, Retail and Ecommerce), By Region - Industry Forecast 2024-2031

Global Software Composition Analysis Market size was valued at USD 398.7 million in 2022 and is poised to grow from USD 483.22 million in 2023 to USD 2249.93 million by 2031, growing at a CAGR of 21.20% during the forecast period (2024-2031).

Software used to protect against potential cyberthreats is called software composition analysis. At each phase of the software development life cycle, the software oversees doing an extensive analysis of software code protection and confirming open-source content. To stop themselves from being controlled by software, companies can recognize and fix software flaws. Examining the constraints of built applications before they are deployed and sold also helps. Through access to third-party code, software composition analysis gives users influence over software over an application's life cycle.

The number of open-source threats and vulnerabilities are rising along with the adoption of open-source codes; yet software composition analysis is becoming more widely accepted as a useful tool for mitigating these risks. Open-source software (OSS) includes benefits like ease of integration, a large selection of components, no cost, and so forth, but it also has drawbacks like the risk of license compliance, security risk, quality risk, and so forth with OSS. Tools for software composition analysis offer advantages like automated license risk exposure checking, information that facilitates fast identification and resolution of vulnerabilities, increased policy implementation flexibility to better align with business requirements, and product integration support for ongoing development processes.