Abstract:
Implementations of the present disclosure include methods, systems, and computer-readable storage mediums for identifying matching elements between a source model and a target model comprising receiving a source model and a target model, the source model and the target model each being stored in computer-readable memory; processing the source model and the target model to generate a plurality of similarity values, each similarity value being associated with an element of the source model and an element of the target model; generating a similarity value construct based on the plurality of similarity values and elements of the source model and the target model; and identifying matching elements between the source model and the target model based on the similarity value construct.

Description:
BACKGROUND 
     Model-driven software development (MDSD) is the automatic (or semi-automatic) generation of software systems from models across multiple stages. This process can entail the step-wise refinement of models, where the last development stage results in the generation of executable source code. This last development step falls into the category of model-to-text transformations and is not limited to source code generation. Generally the output is string-based and any form of text can be possible. The model transformations preceding the last development step are referred to as model-to-model transformations. 
     A model transformation can be executed through a transformation engine, which understands and processes a transformation program, being an instance of a corresponding transformation language. A transformation program defines how an output (e.g. source code of a particular programming language) is constructed depending on model data used as input. To understand which output elements were generated by which rule in the transformation program on the basis of which model input elements, traceability data is employed between the generated output, the transformation program, and the input models. 
     Traceability is the degree to which a relationship can be established between two or more products of the development process, in particular, products having a predecessor-successor or master-subordinate relationship to one another. For example, the degree to which the requirements and design of a given software component match. Traceability data in MDSD can be understood as the runtime footprint of model transformation execution. Trace links provide this kind of information by associating input and output elements with respect to the execution of a certain transformation rules. 
     SUMMARY 
     Implementations of the present disclosure include computer-implemented methods including the actions of identifying matching elements between a source model and a target model. The method comprises receiving a source model and a target model, the source model and the target model each being stored in computer-readable memory; processing the source model and the target model to generate a plurality of similarity values, each similarity value being associated with an element of the source model and an element of the target model; generating a similarity value construct based on the plurality of similarity values and elements of the source model and the target model; and identifying matching elements between the source model and the target model based on the similarity value construct. 
     The implementation can optionally include one or more of the follow features. For instance, the target model is provided based on a transformation of the source model. Also, the method can include converting a data structure of the source model to a data structure of a common data model. Additionally, the method can include converting a data structure of the target model to a data structure of a common data model. Other examples include the plurality of similarity values are based on similarity measures, the similarity measures including attribute similarity measures, connection similarity measures and instance-of similarity measures; the processing further including determining the similarity value for each combination of the elements of the source model and the elements of the target model; the processing further including comparing an attribute of each element of the source model to an attribute of each element of the target model; the processing further including propagating the similarity values of child combinations of elements of the source model and elements of the target model to respective parent combinations of elements of the source model and elements of the target model. Another example includes the processing further including determining, for each combination of elements of the source model and elements of the target model, corresponding combinations of elements of a source meta-model and elements of a target meta-model; and propagating a similarity value of the corresponding combinations of elements of the source meta-model and the elements of the target meta-model to the respective combinations of elements of the source model and elements of the target model. The method further including extracting one or more trace links based on the matching elements, each trace link associating an input element of the source model to an output element of the target model in view of a transformation rule. 
     The present disclosure also provides a computer-readable storage medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations in accordance with implementations of the methods provided herein. 
     The present disclosure further provides a system for implementing the methods provided herein. The system includes one or more processors, and a computer-readable storage medium coupled to the one or more processors having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations in accordance with implementations of the methods provided herein. 
     It is appreciated that methods in accordance with the present disclosure can include any combination of the aspects and features described herein. That is, methods in accordance with the present disclosure are not limited to the combinations of aspects and features specifically described herein, but also include any combination of the aspects and features provided. 
     The details of one or more implementations of the present disclosure are set forth in the accompanying drawings and the description below. Other features and advantages of the present disclosure will be apparent from the description and drawings, and from the claims. 
    
    
     
       DESCRIPTION OF DRAWINGS 
         FIG. 1  is a block diagram of a traceability model matching system. 
         FIG. 2  show instances of a source model and a target model according to respective meta-models. 
         FIG. 3  shows an example of a model matching process utilizing the attribute similarity values and the connection similarity values. 
         FIG. 4  shows an example of a meta-model mapping. 
         FIG. 5  shows an example of a model matching process utilizing the instance-of similarity measures and the meta-model mapping. 
         FIG. 6  is a flowchart of an example process that can be executed in accordance with implementations of the present disclosure. 
         FIG. 7  is a schematic illustration of example computer systems that can be used to execute implementations of the present disclosure. 
     
    
    
     Like reference symbols in the various drawings indicate like elements. 
     DETAILED DESCRIPTION 
     Implementations of the present disclosure are generally directed to generation of trace links in model driven software. In some implementations, a source model can be transformed to a target model. The source model and the target model can each be mapped to graphs that can include elements such as graph nodes (e.g., entities) and data nodes (e.g., attributes). The source model and the target model are input to a traceability model matching system that creates a mapping between the elements of the source model and the target model. This mapping is construed to extract trace links between the source model and the target model. The trace links facilitate determining which target model elements were formed from which source model elements. 
     To determine (e.g., extract) the trace links between the source and target models, the traceability model matching system receives the source and target models. In some implementations, the source and target models are imported into a common data model. In this manner, the source model and the target model have a common basis for matching algorithms, detailed further below. The traceability model matching system processes the source and the target models to generate similarity values between the elements of the source model and the target model. In some examples, the matching algorithms are applied to the source model and the target model to identify model elements that refer to the same conceptual entity. The traceability model matching system generates a similarity value construct based on the similarity values. In some implementations, each matching algorithm provides similarity values for all source and target model element combinations and generates a respective similarity value matrix. The matrices (e.g., one matrix for each matching algorithm) are arranged in a similarity value construct (e.g., a similarity value cube (SVC)). The SVC is configured to derive a mapping (or matches) between the source elements and the target elements. The traceability model matching system identifies matching elements between the source model and the target model based on the similarity value construct. In some examples, the mapping is analyzed and trace links are extracted between the source model and the target model. 
       FIG. 1  is a block diagram of a traceability model matching system  100 . The traceability model matching system  100  includes a model import module  102 , a model matching module  104 , a configuration module  106 , and a trace link extraction module  108 . 
     The model import module  102  receives a source model  110  and a target model  112 . In some examples, the source model  110  and the target model  112  are each transformed to a common data model. In some implementations, the common data model is an internal data model to the system  100 . For example, the source model  110  and the target model  112  can be provided in different model languages. Consequently, the source model  110  and the target model  112  are transformed into a similar format (e.g., a format of the common data model). In some implementations, the system  100  includes an import module  102  for each source model  110  and data model  112 , dependent upon the language of the source model  110  and the data model  112 . Furthermore, as mentioned above, the matching algorithms can be operable on the common data model. Consequently, the source model  110  and the target model  112  are transformed into the common data model such that the matching algorithms can be applied to the source model  110  and the target model  112 . In some implementations, the common data model includes a graph structure. In some examples, the graph structure can facilitate uniform and adequate expression of the source model  110  and the target model  112  and expression of the source model  110  and the target model  112  in relation to corresponding meta-models. In some examples, the graph structure can include type attributed graphs. In some implementations, the system  100  can employ individual matching algorithms for each source model  110  and target model  112 . In some implementations, the source model  110  and/or the target model  112  can be abstractions of text, e.g., software language text. 
     The model matching module  104  process the source model  110  and the target model  112  to generate similarity values between elements of the source model  110  and the target model  112 . To that end, model matching is employed to find a mapping between the source model  110  and the target model  112  based on a given similarity measure. For each source-target element combination, a similarity value is determined according to a predefined similarity measure. The resulting similarities are analyzed and matches are derived between the source-target element combinations. In some examples, different similarity measures derive differing similarity values between each source-target element combination. For example, each source element is compared with each target element employing the matching algorithms, and for each comparison, similarity values can be determined. Based on the similarity values between each source-target element combination, it is determined whether the source-target element combinations are matched. To that end, and in some implementations, the matching algorithms can include an attribute similarity measure (ATTRIBUTES), a connection similarity measure (CONNECTIONS), and an instance-of similarity measure (INSTANCEOF). In some examples, the similarity values can be between 0 and 1, where, the higher the similarity value, the higher the similarity between the source-target element combinations. 
     In some implementations, the attribute similarity measure includes determining similar elements from the source model  110  and the target model  112  by indication of shared attributes (e.g., characteristics or values) of the source model  110  and the target model  112  elements. The model matching module  104  compares the attributes of each source element to the attributes of each target element, and dependent on the attribute similarity measure algorithm employed, the model matching module  104  determines a similarity value for each source-target element combination based on the attributes of the source-target element combination. For each source-target graph node combination containing data nodes, the attribute similarity value is determined. In some examples, for each source-target graph node combination, the data (e.g., attribute) nodes of the source graph node and the data (e.g., attribute) nodes of the target graph node are determined. The attribute similarity measure is determined for each source-target attribute data node combination for each source-target graph node combination and is stored in an attribute similarity matrix. In some examples, the attribute similarity matrix is reduced to a single attribute similarity value dependent upon the selection strategy applied to the attribute similarity matrix. This single attribute similarity value is the attribute similarity value between two given source-target graph nodes. In some implementations, the data nodes can include labels (i.e., strings). 
     In some implementations, the connection similarity measure includes propagating the similarity value of child source-target graph node combinations to parent source-target graph node combinations. In other words, the source-target parent graph node combinations inherent the similarity values of its child source-target graph node combinations. For example, two parent graph nodes from the source model  110  and the target model  112  that have similar children graph nodes will likely refer to the same entity. For each parent source-target graph node combination, the child graph nodes of the source parent graph node and the child graph nodes of the target parent graph node are determined. The combination similarity measure is determined for each source-target child graph node combination of each source-target parent graph node combination and placed in a connection similarity matrix. The combination similarity matrix is reduced to a single combination value dependent upon the selection strategy applied to the connection similarity matrix. This single connection similarity value is the connection similarity value between two given parent source-target graph nodes. 
     Example algorithms that can be used in implementations of the present disclosure include algorithms A-D, provided below. 
     
       
         
               
             
               
               
             
               
               
             
               
               
             
               
               
               
             
               
               
               
             
               
               
               
             
               
             
               
               
               
             
               
               
               
             
               
               
             
               
               
               
             
               
               
               
             
               
               
               
             
               
             
               
               
               
             
               
               
               
             
               
               
             
               
               
             
           
               
                   
               
             
             
               
                 Require: compare : D S   1  × D S   2  → similiarityValues 
               
               
                 Ensure: dataNode source  ∈ D S   1    ⊂  V D   1   
               
               
                 Ensure: dataNode target  ∈ D S   2    ⊂  V D   2   
               
             
          
           
               
                   
                 1: function COMPUTESIMILIARITY (dataNode source,  dataNode target ) 
               
               
                   
                 2: returncompare (dataNode source,  dataNode target ) 
               
               
                   
                 3: end function 
               
             
          
           
               
                   
                 Ensure: s i  ∈ V G   1   
               
               
                   
                 Ensure: t j  ∈ V G   2   
               
             
          
           
               
                   
                 4: function MATCHATTRIBUTES (s i , t j ) 
               
             
          
           
               
                   
                 5: 
                 Nodes source   ATT ← {target NA   1  (e) | e ∈ E NA   1  and source NA   1  (e) = s i } 
               
               
                   
                 6: 
                 Nodes target   ATT ← {target NA   2  (e) | e ∈ E NA   2  and source NA   2  (e) = t j } 
               
               
                   
                 7: 
                 for all s i     k    ∈ Nodes source   ATT  , k = {1,..., |V D   1 |} do 
               
             
          
           
               
                   
                 8: 
                 for all t j     l    ∈ Nodes target   ATT , l = {1,..., | V D   2 |} do 
               
             
          
           
               
                   
                 9: 
                 SIM Max(k)Max(l)   ATT             sim i     k     j     l   ← 
               
             
          
           
               
                 computeSimiliarity(s i     k   , t j     l   ) 
               
             
          
           
               
                   
                 10: 
                 end for 
               
             
          
           
               
                   
                 11: 
                 end for 
               
               
                   
                 12: 
                 return computeSetSimiliarity(SIM Max(k)Max(l)   ATT ) 
               
             
          
           
               
                   
                 13: end function 
               
               
                   
                 14: function MATCHCONNECTEDNODES (s i , t j ) 
               
             
          
           
               
                   
                 15: 
                 Nodes source   Con ← {target G   1  (e) | e ∈ E G   1  and source G   2  (e) = s i } 
               
               
                   
                 16: 
                 Nodes target   CON ← {target G   2  (e) | e ∈ E G   2  and source G   2  (e) = t j } 
               
               
                   
                 17: 
                 for all s i     p    ∈ Nodes source   CON  , p = {1,..., | V G   1 | − 1} do 
               
             
          
           
               
                   
                 18: 
                 for all t j     q    ∈ Nodes target   CON  , q = {1,..., | V G   2 | − 1} do 
               
             
          
           
               
                   
                 19: 
                 SIM Max(p)Max(q)   CON             sim i     p     j     q   ← 
               
             
          
           
               
                 match Attributes (s i     p   , t j     q   ) 
               
             
          
           
               
                   
                 20: 
                 end for 
               
             
          
           
               
                   
                 21: 
                 end for 
               
               
                   
                 22: 
                 return computeSetSimiliarity(SIM Max(p)Max(q)   CON ) 
               
             
          
           
               
                   
                 23: end function 
               
             
          
           
               
                   
                 End 
               
               
                   
               
             
          
         
       
     
     Algorithm A: determining an attribute similarity value and a connection similarity value 
     
       
         
               
               
             
               
               
             
               
               
               
             
               
               
               
             
               
               
               
             
               
             
               
               
               
             
               
               
               
             
               
               
             
               
               
             
               
               
             
               
               
               
             
               
               
               
             
               
               
               
             
               
               
               
             
               
               
               
             
               
               
             
               
               
             
           
               
                   
               
             
             
               
                   
                 Require: AG 1  = (G 1 , D 1 ) and AG 2  = (G 2 , D 1 ) 
               
               
                   
                 Require: G 1  = (V G   1 , V D   1 , E G   1 , E NA   1 , E EA   1 , (source j   1 , target j   1 ) j ∈  {G, NA, EA} ) 
               
               
                   
                 Require: G 2  = (V G   2 , V D   2 , E G   2 , E NA   2 , E EA   2 , (source j   2 , target j   2 )j∈  {G, NA, EA} ) 
               
               
                   
                 Require: D 1  = (S D   1 , OP D   1 ) and D 2  = (S D   2  OP D   2 ) 
               
               
                   
                 Require: similarityValues = {r|r ∈            and 0 ≦ r ≦ 1} ∪ {UNKNOWN} 
               
               
                   
                 Ensure: similarityMeasure ∈ {ATTRIBUTES, CONNECTIONS} 
               
             
          
           
               
                   
                 1: procedure MATCHGRAPHS (AG 1 , AG 2 , similarityMeasure) 
               
             
          
           
               
                   
                 2: 
                 for all s i  ∈ V G   1  , i = {1,..., | V G   1 |} do 
               
             
          
           
               
                   
                 3: 
                 for all t j  ∈ V G   2 ,j = {1,..., V G   2 |} do 
               
             
          
           
               
                   
                 4: 
                 SIM |V     G       1     ||V     G       2     |             sim ij ← 
               
             
          
           
               
                 matchNodes(s i , t j , similarityMeasure) 
               
             
          
           
               
                   
                 5: 
                 end for 
               
             
          
           
               
                   
                 6: 
                 end for 
               
               
                   
                 7: 
                 matches ← retrieveMatches SIM |V     G       1     ||V     G       2     |   
               
             
          
           
               
                   
                 8: end procedure 
               
             
          
           
               
                   
                 Ensure: s i  ∈ V G   1   
               
               
                   
                 Ensure: t j  ∈ V G   2   
               
             
          
           
               
                   
                 9: function MATCHNODES (s i , t j , similarityMeasure) 
               
             
          
           
               
                   
                 10: 
                 if similiarityMeasure = ATTRIBUTES then 
               
             
          
           
               
                   
                 11: 
                 return matchAttributes (s t , t j ) 
               
             
          
           
               
                   
                 12: 
                 else if similiarityMeasure = CONNECTIONS then 
               
             
          
           
               
                   
                 13: 
                 return matchConnectedNodes (s i , t j ) 
               
             
          
           
               
                   
                 14: 
                 end if 
               
               
                   
                 15: 
                 return UNKNOWN 
               
             
          
           
               
                   
                 16: end function 
               
             
          
           
               
                   
                 end 
               
               
                   
               
             
          
         
       
     
     Algorithm B: mapping between the source model and the target model 
     
       
         
               
               
             
               
               
               
             
               
             
               
               
             
           
               
                   
               
             
             
               
                   
                 Ensure: s i  ∈ V G   1 and Node source   ATT _Type ∈ V G   1   
               
               
                   
                 Ensure: tj ∈ V G   2 and Node target   ATT _Type ∈ V G   2   
               
               
                   
                 Ensure: typeSimilarityMeasure ∈ similarityMeasure \ 
               
               
                   
                 Ensure: typeSimilarityMeasure ∈ similarityMeasure \ {INSTANCEOF} 
               
               
                   
                 1: function MATCHNODETYPES (s i , t j ) 
               
             
          
           
               
                   
                 2: 
                 Nodes source   ATG _Type← t 1 (s i ) 
               
               
                   
                 3: 
                 Nodes target   ATG _Type← t 2 (t j ) 
               
               
                   
                 4: 
                 return matchNodes (Node source   ATT _Type, Node target   ATT _Type, 
               
             
          
           
               
                 typeSimilarityMeasure) 
               
             
          
           
               
                   
                 5: end function 
               
               
                   
                 end 
               
               
                   
               
             
          
         
       
     
     Algorithm C: determining the instance-of similarity value 
     
       
         
               
             
               
               
             
               
               
               
             
               
               
             
               
             
               
               
             
               
               
               
             
               
               
               
             
               
               
               
             
               
               
               
             
               
               
               
             
               
               
               
             
               
               
               
             
               
               
             
               
             
           
               
                   
               
             
             
               
                   Require: AG 1  = (G 1 , D 1 ) and AG 2  = (G 2 , D 2 ) 
               
               
                   Require: G 1  = (V G   1 , V D   1 , E G   1 , E NA   1 , E EA   1 , (source j   1 , targetj 1 ) j ∈  {G, NA, EA} ) 
               
               
                   Require: G 2  = (F G   2 , F D   2 , E G   2 , E NA   2 , E EA   2 , (source j   2 , target j   2 ) j ∈  {G, NA, EA} ) 
               
               
                   Require: ATG 1  = (TG 1 , Z 1 ) with Z 1  as final algebra of D 1   
               
               
                   Require: ATG 2  = (TG 2 , Z 2 ) with Z 2  as final algebra of D 2   
               
               
                   Require: TG 1  = (G 1 , Z 1 ) and TG 2  = (G 2 , Z 2 ) 
               
               
                   Require: G 1  = (V G   1 , V D   1 , E G   1 , E NA   1 , E EA   1 , (Source j   1 , Target j   1 ) j  ∈  {G,NA, EA} ) 
               
               
                   Require: G 2  = (V G   2 , V D   2 , E G   2 , E NA   2 , E EA   2 , (Source j   2 , Target j   2 ) j ∈  {G, NA, EA} ) 
               
               
                   Require: TAG 1  = (AG 1 ,t 1 ) over ATG 1  with t 1  : AG 1 → ATG 1   
               
               
                   Require: TAG 2  = (AG 2 ,t 2 } over ATG 2  with t 2  : AG 2 → ATG 2   
               
               
                   Require: similarityValues = {r|r ∈             and r ≧ 0 and r ≦ 1} ∪ {UNKNOWN} 
               
               
                   Ensure: similarityMeasure ∈ {ATTRIBUTES, CONNECTIONS, INSTANCEOF} 
               
             
          
           
               
                   
                 1: procedure MATCHTYPEDGRAPHS (TAG 1 , TAG 2 , similarityMeasure) 
               
             
          
           
               
                   
                 2: 
                 matchGraphs (AG 1 , AG 2 , similarityMeasure) 
               
             
          
           
               
                   
                 3: end procedure 
               
             
          
           
               
                   Ensure (s i , t j ) ∈ (V G   1  × V G   2  ∪ (V G   1  × V G   2 ) 
               
             
          
           
               
                   
                 4: function MATCHNODES ((s i , t j ), similarity Measure) 
               
             
          
           
               
                   
                 5: 
                 if similiarityMeasure = ATTRIBUTES then 
               
             
          
           
               
                   
                 6: 
                 return matchAttributes (s i , t j ) 
               
             
          
           
               
                   
                 7: 
                 else if similiarityMeasure = CONNECTIONS then 
               
             
          
           
               
                   
                 8: 
                 return matchConnectedNodes (s i , t j ) 
               
             
          
           
               
                   
                 9: 
                 else if similiarityMeasure = INSTANCEOF then 
               
             
          
           
               
                   
                 10: 
                 return matchNodeTypes (s i , t j ) 
               
             
          
           
               
                   
                 11: 
                 end if 
               
               
                   
                 12: 
                 return UNKNOWN 
               
             
          
           
               
                   
                 13: end function 
               
             
          
           
               
                   end 
               
               
                   
               
             
          
         
       
     
     Algorithm D: mapping between the source model and the target model 
     G indicates a graph, AG indicates an attributed graph, D S  indicates a data sort (a set of data nodes of the same sort, that is, with labels of the same language, e.g. String), D indicates an algebraic signature, where S D  indicates the set S of sorts for D, and OP D  indicates a family of n-ary operations on S D , Z indicates the final algebra of D, ATG indicates an attributed typed graph, V indicates a set of graph nodes (V G  graph nodes, V D  data nodes), E indicates a set of graph edges (E G  usual graph edges and special edges E NA  and E EA  used for node resp. edge attribution), and TAG indicates a typed attributed graph. Also, italicized passages indicate reference to the meta-model. Example algorithm A provides for determining an attribute similarity value and a connection similarity value. 
     The attribute similarity value and the connection similarity value can be employed to match the source model  110  and the target model  112  and provide a mapping between the source and the target graph nodes. Example algorithm B provides for mapping between the source model  110  and the target model  112 . Algorithm B utilizes the matchAttributes and the matchConnectedNodes functions provided in Algorithm A. 
     The instance-of similarity measure includes matching graph nodes of the source model  110  and the target model  112  based on matching between source and target meta-models. In other words, the instance-of similarity measure includes propagating the similarity of meta-model nodes to respective model nodes. Specifically, for each source-target graph node combination, the corresponding meta-model graph nodes are determined. For each meta-model graph node combination, an instance-of similarity value is determined. The instance-of similarity value of each meta-model source-target graph node combination is propagated to the corresponding model source-target graph node combination and placed in an instance-of similarity matrix. The instance-of similarity matrix is reduced to a single instance-of similarity value dependent upon the selection strategy applied to the instance-of similarity matrix. This single instance-of similarity value is the instance-of similarity value between two given source-target graph nodes. 
     Example algorithm C provides for determining the instance-of similarity value. Algorithm C employs the matchNodes function provided in Algorithm B. 
     The instance-of similarity value can be employed to match the source model  110  and the target model  112  and provide a mapping between the source and the target graph nodes. Example algorithm D provides for mapping between the source model  110  and the target model  112 . Algorithm D employs the matchGraphs function defined in Algorithm B and the matchNodeTypes function defined in Algorithm C. 
     In some implementations, additional matching algorithms can also be in employed by the model matching module  104  in conjunction with the above-mentioned matching algorithms. For example, a graph edit distance (GED) algorithm and/or a pattern matcher algorithm can be employed. The GED algorithm determines a similarity of two given source-target element combinations based on a connection of the source-target element combination in a common subgraph. The pattern matcher algorithm determines a similarity of two given source-target element combinations based on a recurring pattern in the source and the target model. 
     The configuration module  106  generates a similarity value construct based on the similarity measures. Specifically, each matching algorithm provides similarity values between elements of the source model  110  and the target model  112 , with the similarity values placed in a respective similarity value matrix. The similarity value matrices are arranged into a similarity value construct, or a similarity value cube (SVC). To provide a mapping (or matching) between source-target element combinations from the SVC, the SVC is configured to form an aggregation matrix by either determining the average of the similarity values or by selecting all similarity values exceeding a threshold. Further, the selection of how the SVC is configured can affect the quality of matching results. 
     The trace link extraction module  108  matches elements between the source model  110  and the target model  112  based on the similarity value construct. Specifically, the trace link extraction module  108  analyzes the mapping and extracts trace links  114  between the source model  110  and the target model  112 . For example, a trace link  114  can be extracted between the source model  110  and the target model  112  when the similarity value, as provided in the SVC, between a source-target element combination is above a threshold. 
       FIG. 2  show instances of a source model  202  and a target model  206  according to respective meta-models. Specifically, the source model  202  is an instance of the source meta-model  204  and the target model  206  is an instance of the target meta-model  208 . The source model  202  and the target model  206  can be imported to the system  100  by the model import module  102 . The source model  202  and the target model  206  can be analogous to the source model  110  and the target model  112 , respectively, mentioned above with respect to  FIG. 1 . 
     The source meta-model  204  includes an entity graph node  210  that has a child feature graph node  212 . The entity graph node  210  and the feature graph node  212  both contain a string data node  214 . Further, the source model  202  includes an entity graph node  216  that contains a data node  218  of “person.” The entity graph node  216  has child nodes of feature 1  graph node  220  and feature 2  graph node  222 . Feature 1  graph node  220  contains a data node  224  of “name” and feature 2  graph node  222  contains a data node  226  of “age.” 
     The target meta-model  208  includes a class graph node  250  that has a child field graph node  252  and a child method graph node  254 . The class graph node  250 , the child field graph node  252 , and the child method graph node  254  contain a string data node  256 . Further, the target model  206  includes a class graph node  258  that contains a data node  260  of “person.” The class graph node  258  has child nodes of field 1  graph node  262 , field 2  graph node  264 , method 1  graph node  266 , and method 2  graph node  268 . Field 1  graph node  262  contains a data node  270  of “name;” field 2  graph node  264  contains a data node  272  of “age;” method 1  graph node  266  contains a data node  274  of “getName;” and method 2  graph node  268  contains a data node  276  of “getAge.” 
       FIG. 3  shows an example of a model matching process utilizing the attribute similarity values and the connection similarity values. Specifically,  FIG. 3  includes mappings between the source model  202  and the target model  206 . To that end, employing Algorithms A and B, the product of the set of all source graph nodes (e.g., entity graph node  216 , feature 1  graph node  220 , and feature 2  graph node  222 ) and the set of all target graph nodes (e.g., class graph node  258 , field 1  graph node  262 , field 2  graph node  264 , method 1  graph node  266 , and method 2  graph node  268 ) is determined, providing a similarity matrix (for each of the attribute similarity values and of the connection similarity values) of fifteen cells for each source-target graph node combination. The similarity values can be based on the attribute similarity values and/or the connection similarity values. 
     To that end, when the similarity values are based on the attribute similarity values, the data nodes of each source-target graph node combination are retrieved, and the attribute similarity values are determined. The attribute similarity values are dependent on the specific algorithm employed, (e.g., the computeSimiliarity function of Algorithm A). In some examples, the similarity values are determined such that, for given data sorts D S   1 =D S   1 =String, with vεD S   1  and wεD S   2 , the function compare: D S   1 ×D S   2 →similarityValues can be defined as: 
     
       
         
           
             
               compare 
               ⁡ 
               
                 ( 
                 
                   v 
                   , 
                   w 
                 
                 ) 
               
             
             = 
             
               { 
               
                 
                   
                     
                       1 
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                         if 
                         ⁢ 
                         
                             
                         
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                         v 
                       
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                       w 
                     
                   
                 
                 
                   
                     
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     Continuing the example, for the source-target graph node combination of entity graph node  216  and class graph node  258 , the data nodes  218  and  260  are retrieved. Since the labels (i.e., strings) of data nodes  218  and  260  are identical (e.g., contain the attribute “person”), a similarity value of 1 (or any value between 0 and 1) is assigned to the source-target graph node combination of entity graph node  216  and class graph node  258 . Analogously, for the source-target graph node combination of feature 1  graph node  220  and class graph node  258 , a similarity value of 0 is determined, since the data node  224  and the data node  260  are not identical (the data node  224  contains the attribute “name” and the data node  260  contains the attribute “person”). Similarly, the above compare function can be applied to each of the remaining source-target graph node combination of the source model  202  and the target model  206 , as shown by Table 1. 
     
       
         
               
             
               
               
               
               
               
               
             
               
               
               
               
               
               
               
             
           
               
                 TABLE 1 
               
             
             
               
                   
               
               
                 Example similarity values of graph nodes. 
               
             
          
           
               
                   
                 class 
                 field1 
                 field2 
                 method1 
                 method2 
               
               
                   
                   
               
             
          
           
               
                   
                 entity 
                 1 
                 0 
                 0 
                 0 
                 0 
               
               
                   
                 feature1 
                 0 
                 1 
                 0 
                 0.5 
                 0 
               
               
                   
                 feature2 
                 0 
                 0 
                 1 
                 0 
                 0.5 
               
               
                   
                   
               
             
          
         
       
     
     The resulting mapping of the source-target graph node combinations is depicted in  FIG. 3 . Specifically, line  302  includes a value of 1 and represents the similarity value between entity graph node  216  and class graph node  258 ; line  304  includes a value of 1 and represents the similarity value between feature 1  graph node  220  and field 1  graph node  262 ; line  306  includes a value of 1 and represents the similarity value between feature 2  graph node  222  and field 2  graph node  264 ; line  308  includes a value of 0.5 and represents the similarity value between feature 1  graph node  220  and method 1  graph node  266 ; and line  310  includes a value of 0.5 and represents the similarity value between feature 2  graph node  222  and method 2  graph node  268 . 
     To that end, when the similarity values are based on the connection similarity measure, the children graph nodes of each source-target graph node combination are retrieved, and the similarity values are determined. For example, the feature 1  graph node  220  and feature 2  graph node  222  are retrieved for the entity graph node  216  and field 1  graph node  262 , field 2  graph node  264 , method 1  graph node  266 , and method 2  graph node  268  are retrieved for the class graph node  258 . To that end, for each child source-target graph node combination, a similarity value is determined based on the similarity values of Table 1, resulting in a similarity value matrix. In some implementations, the similarity value matrix is reduced to a singularly value by taking the average of all values from corresponding child source-target graph node combinations. Continuing the example, a similarity value of 0.75 (average of lines  304 - 310 ) is determined and propagated to the parent source-target graph node combination of entity graph node  216  and class graph node  258 , denoted by line  312 . 
       FIG. 4  shows an example of a meta-model mapping. Specifically,  FIG. 4  includes mappings between the source meta-model  204  and the target meta-model  208 . Specifically, the entity graph node  210  of the source meta-model  204  is mapped to the class graph node  250  of the target meta-model  208 . Further, the feature graph node  212  of the source meta-model  204  is mapped to the field graph node  252  and the method graph node  254  of the target meta-model  208 . 
       FIG. 5  shows an example of a model matching process utilizing the instance-of similarity measures and the meta-model mapping of  FIG. 4 . Specifically,  FIG. 5  includes mappings between the source model  202  and the target model  206 . To that end, employing Algorithms C and D, the product of the set of all source graph nodes (e.g., entity graph node  216 , feature 1  graph node  220 , and feature 2  graph node  222 ) and the set of all target graph nodes (e.g., class graph node  258 , field 1  graph node  262 , field 2  graph node  264 , method 1  graph node  266 , and method 2  graph node  268 ) is determined, providing an instance-of similarity matrix of fifteen cells for each source-target graph node combination. 
     For each source-target graph node combination, a similarity value is determined between the nodes of the source meta-model  204  and the target meta-model  208 . For example, the entity graph node  210  of the source meta-model  204  corresponds to the entity graph node  216  of the source model  202 . Further, since a mapping exists between the entity graph node  210  of the source meta-model  204  and the class graph node  250  of the target meta-model  208 , the similarly value of 1 (or any value between 0 and 1) is propagated to the corresponding source-target graph node combination of the entity graph node  216  of the source model  202  and the class graph node  258  of the target model  208 . Similarly, the similarity of the entity graph node  216  of the source model  202  and the field graph node  262  of the target model  206  can be determined. However, since the entity graph node  210  of the source meta-model  204  and the field graph node  252  of the target meta-model  208  do not match, the similarity of the source-target graph node combination of the entity graph node  216  and the field graph node  262  is 0. Similarly, the above can be applied to the remaining source-target graph node combinations of the source model  202  and the target model  206  and propagated to the corresponding source-target graph node combinations of the source model  202  and the target model  208 , as shown by Table 2. 
     
       
         
               
             
               
               
               
               
               
               
             
               
               
               
               
               
               
               
             
           
               
                 TABLE 2 
               
             
             
               
                   
               
               
                 Example similarity values of AG nodes. 
               
             
          
           
               
                   
                 class 
                 field1 
                 field2 
                 method1 
                 method2 
               
               
                   
                   
               
             
          
           
               
                   
                 entity 
                 1 
                 0 
                 0 
                 0 
                 0 
               
               
                   
                 feature1 
                 0 
                 1 
                 1 
                 1 
                 1 
               
               
                   
                 feature2 
                 0 
                 1 
                 1 
                 1 
                 1 
               
               
                   
                   
               
             
          
         
       
     
       FIG. 6  is a flowchart of an example process  600  that can be executed in accordance with implementations of the present disclosure. The example process  600  can be executed using one or more computing devices. 
     The source model and the target model are received ( 602 ). For example, the model import module  102  receives the source model  110  (or the source model  202 ) and the target model  112  (or the target model  206 ). In some implementations, the source model  110  and the target model  112  data can be retrieved from computer-readable memory. The source model and the target model are imported ( 604 ). For example, the model import module  102  imports source model  110  (or the source model  202 ) and the target model  112  (or the target model  206 ) into a common data model of the system  100 . Similarity values between the source model and the target model are determined ( 606 ). For example, the model matching module  104  generates similarity values between the source model  110  (or the source model  202 ) and the target model  112  (or the target model  206 ). The similarity values can include, among others, the attribute similarity values, the connections similarity values, and the instance-of similarity values. Further, each matching algorithm of the model matching module  104  provides similarity values for all source-target model node combinations and generates a respective similarity value matrix. The SVC is constructed ( 608 ). For example, the configuration module  106  constructs the SVC based on the similarity values between the source model  110  (or the source model  202 ) and the target model  112  (or the target model  206 ). To provide a mapping (or matching) between source-target node combinations from the SVC, the SVC is configured to form an aggregation matrix by either determining the average of the similarity values or by selecting all similarity values exceeding a threshold. Match(es) between elements of the source model and the target model are identified ( 610 ). For example, the trace link extraction module  108  identifies matches between nodes of the source model  110  (or the source model  202 ) and the target model  112  (or the target model  206 ) based on the SVC. Trace links are extracted between the source model and the target model ( 612 ). For example, the trace link extraction module  108  analyzes the match(es) between elements of the source model  110  (or the source model  202 ) and the target model  112  (or the target model  206 ), and extracts trace links based on these match(es). 
     Referring now to  FIG. 7 , a schematic diagram of an example computing system  800  is provided. The system  700  can be used for the operations described in association with the implementations described herein. For example, the system  700  may be included in any or all of the server components discussed herein. The system  700  includes a processor  710 , a memory  720 , a storage device  730 , and an input/output device  740 . Each of the components  710 ,  720 ,  730 , and  740  are interconnected using a system bus  750 . The processor  710  is capable of processing instructions for execution within the system  700 . In one implementation, the processor  710  is a single-threaded processor. In another implementation, the processor  710  is a multi-threaded processor. The processor  710  is capable of processing instructions stored in the memory  720  or on the storage device  730  to display graphical information for a user interface on the input/output device  740 . 
     The memory  720  stores information within the system  700 . In one implementation, the memory  720  is a computer-readable medium. In one implementation, the memory  720  is a volatile memory unit. In another implementation, the memory  720  is a non-volatile memory unit. The storage device  730  is capable of providing mass storage for the system  700 . In one implementation, the storage device  730  is a computer-readable medium. In various different implementations, the storage device  730  may be a floppy disk device, a hard disk device, an optical disk device, or a tape device. The input/output device  740  provides input/output operations for the system  700 . In one implementation, the input/output device  740  includes a keyboard and/or pointing device. In another implementation, the input/output device  740  includes a display unit for displaying graphical user interfaces. 
     The features described can be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations of them. The apparatus can be implemented in a computer program product tangibly embodied in an information carrier, e.g., in a machine-readable storage device, for execution by a programmable processor; and method steps can be performed by a programmable processor executing a program of instructions to perform functions of the described implementations by operating on input data and generating output. The described features can be implemented advantageously in one or more computer programs that are executable on a programmable system including at least one programmable processor coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system, at least one input device, and at least one output device. A computer program is a set of instructions that can be used, directly or indirectly, in a computer to perform a certain activity or bring about a certain result. A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. 
     Suitable processors for the execution of a program of instructions include, by way of example, both general and special purpose microprocessors, and the sole processor or one of multiple processors of any kind of computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for executing instructions and one or more memories for storing instructions and data. Generally, a computer will also include, or be operatively coupled to communicate with, one or more mass storage devices for storing data files; such devices include magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and optical disks. Storage devices suitable for tangibly embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, ASICs (application-specific integrated circuits). 
     To provide for interaction with a user, the features can be implemented on a computer having a display device such as a CRT (cathode ray tube) or LCD (liquid crystal display) monitor for displaying information to the user and a keyboard and a pointing device such as a mouse or a trackball by which the user can provide input to the computer. 
     The features can be implemented in a computer system that includes a back-end component, such as a data server, or that includes a middleware component, such as an application server or an Internet server, or that includes a front-end component, such as a client computer having a graphical user interface or an Internet browser, or any combination of them. The components of the system can be connected by any form or medium of digital data communication such as a communication network. Examples of communication networks include, e.g., a LAN, a WAN, and the computers and networks forming the Internet. 
     The computer system can include clients and servers. A client and server are generally remote from each other and typically interact through a network, such as the described one. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. 
     In addition, the logic flows depicted in the figures do not require the particular order shown, or sequential order, to achieve desirable results. In addition, other steps may be provided, or steps may be eliminated, from the described flows, and other components may be added to, or removed from, the described systems. Accordingly, other implementations are within the scope of the following claims. 
     A number of implementations of the present disclosure have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the present disclosure. Accordingly, other implementations are within the scope of the following claims.