The　implementation　of　large-scale　software　systems　usually　depends　on　low-level　frameworks,　or　third-party　libraries.　However,　the　evolution　of　these　frameworks　or　libraries　is　independent　of　the　upper-level　applications,　which　brings　challenges　in　upper-level　code　quality　assurance.　For　instance,　changcs　in　framework/library　code,　such　as　adding　or　removing　APIs　and　altering　API　semantics,　may　result　in　inconsistencies　among　different　versions　of　the　framework/library　codc.　These　inconsistencies　can　impact　the　quality　of　higher-level　apps　when　developers　update　frameworks/libraries.　To　address　this　issue,　analyzing　the　evolution　process　of　framework/library　codc　APIs　is　essential.　This　analysis　helps　upper-level　app　developers　swiftly　choose　compatible　versions　or　adjust　their　code.　In　this　contcxt.　analyzing　the　evolution　proccss　corresponds　to　constructing　a　framework　API　lifccycle　model.　Nowadays,　existing　works　propose　the　API　existence-changing　model　for　defect　detection,　while　not　considering　the　influence　of　semantic　changes　in　APIs,　especially　exception-related　code　evolution.　To　fill　this　gap,　this　paper　adopts　static　analysis　techniques　to　cxtract　exception　summary　information　in　the　framework　API　code,　proposes　a　multi-step　matching　strategy　to　obtain　the　changing　process　of　exceptions,　and　finally　generates　exception-aware　API　lifecycle　models　for　the　given　framework/library　project.　Our　approach:　(1)　adopts　control-dependency　slicing　analysis　to　cxtract　the　conditions　of　the　ex-　ception-thrown　statements;　uses　a　parameter　tracing　strategy　to　transform　exception-throwing　conditions　into　external-variable-related　preconditions;　and　performs　inter-procedure　precondition　construction　by　a　bottom-up　summary-based　analysis.　(2)　proposes　the　exact-matching　and　adap-　tive-matching　strategies　to　analyze　the　framework/library　codc　changes　including　additions,　deletions　and　modifications　of　APIs;　generates　exception-aware　API　lifecycle　models　which　covcr　seven　API　changing　types.　With　this　approach,　the　API　lifecycle　extraction　tool,　JavaExP.　is　implemented.　which　is　based　on　Java　bytecodc　analysis.　Compared　to　the　state-of-the-art　tool,　the　F1　scorc　of　cxccption　summary　information　extracted　by　JavaExP　has　increased　by　67.%,　with　a　reduction　in　processing　time　by　87%.　The　evaluation　of　real-world　projects　shows　that,　compared　to　the　exception-unaware　API　lifecycle　modeling.　JavaExP　can　identify　18.%　times　more　API　changes.　Among　the　75,433　APIs　under　analysis,　20%　of　APIs　have　changed　their　exception-　throwing　behavior　at　least　once　after　API　introduction.　These　APIs　involve　a　total　of　more　than　7K　independent　cxccption　changcs.　The　overall　results　show　that　the　exception-aware　lifecycle　modeling　can　dcscribc　the　evolution　proccss　of　APIs　more　accurately.　©　2024　Science　Press.　All　rights　reserved.