A Metric-based Approach to Detect Abstract Data Types and State Encapsulations

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1 Copyright by IEEE. Published i Proceedigs of the Coferece o Automated Software Egieerig, IEEE Computer Society. A Metric-based Approach to Detect Abstract Data Types ad State Ecapsulatios Jea-Fraçois Girard Frauhofer Istitute for Experimetal Software Egieerig Sauerwiese 6 D Kaiserslauter, Germay girard@iese.fhg.de Abstract This article presets a approach to idetify abstract data types (ADT) ad abstract state ecapsulatios (ASE, also called abstract objects) i source code. This approach groups together fuctios, types, ad variables ito ADT ad ASE cadidates accordig to the proportio of features they share. The set of features cosidered icludes the cotext of these elemets, the relatioships to their eviromet, ad iformal iformatio. A prototype tool has bee implemeted to support this approach. It has bee applied to three C systems (each betwee Kloc). The ADTs ad ASEs idetified by the approach are compared to those idetified by software egieers who did ot kow the proposed approach. I a case study, this approach has bee show to idetify, i most cases, more ADTs ad ASEs tha five published techiques applied o the same systems. This is importat whe tryig to idetify as may ADTs ad ASEs as possible. 1. Itroductio The Bauhaus project 1 aims at recoverig the software architecture of a system as it is implemeted. That is, recoverig multiple views which would idicate the mai compoets of the system, their coectors (how they commuicate), ad the costraits o these coectors ad compoets. This article presets a approach which ca be used as a first step i compoets idetificatio. A case study [Gira97a] suggests that abstract data types ad abstract 1. Research collaboratio betwee the Frauhofer IESE ad the Uiversity of Stuttgart. Raier Koschke, Georg Schied Uiversity of Stuttgart Breitwiesestr D Stuttgart, Germay {koschke, schied}@iformatik.ui-stuttgart.de state ecapsulatios are two istaces of the smallest compoets which are sigificat at the architectural level. The authors have amed such buildig blocks atomic compoets, because they ca be used to build larger compoets. Accordig to Sommerville [Somm92] a abstract data type (ADT) is a abstractio of a type which ecapsulates all the type s valid operatios ad hides the details of the implemetatio of those operatios by providig access to the types exclusively through a well-defied set of operatios. A abstract state ecapsulatio (ASE) is a group of global variables together with the routies which access them. These clusters are also called abstract objects [Ghez91] or object istaces [Yeh95]. Cross-breedigs of ADTs ad ASEs are called hybrid compoets. They cosist of routies, variables, ad types. These three types of atomic compoets are importat to the authors research o architecture recovery, but they are also of geeral importace. Their role has bee recogized to provide iformatio hidig ad thus to support maitaiability [Ghez91] ad reuse [Somm92]. However, these atomic compoets are ot always explicitly captured i the source code, because programmig laguages like C do ot provide good support to express them or software developmet does ot always exploit them. Cosequetly, reverse egieerig techiques are required to idetify them i these circumstaces. This article presets a reverse egieerig approach, called similarity clusterig, which groups together fuctios, types, ad variables ito ADT or ASE cadidates accordig to a similarity metric. The set of features cosidered by this metric icludes the cotext of these elemets, the relatioships betwee these elemets, ad

2 iformal iformatio. I order to evaluate the proposed similarity clusterig approach, it was applied to three C systems (betwee Kloc) ad the results were compared to the atomic compoets idetified by software egieers. Five other published atomic compoet idetificatio techiques were applied to these systems. O this bechmark, similarity clusterig approach idetifies, i most cases, more correct ASEs ad ADTs tha the other techiques. This is importat i order to recover as may ADTs ad ASEs as possible. Paper Overview The remaider of the article is orgaized as follows: Sectio 2 itroduces the system graph abstractio used to defie the similarity metric at the core of the proposed approach. Sectio 3 presets the approach. Sectio 4 presets related research. Sectio 5 describes the experimet setup used to evaluate atomic compoets idetified by automatic techiques. Sectio 6 discusses results ad summarizes the results of a case study [Gira97b] comparig some of them to the proposed approach. Sectio 7 cocludes ad proposes further research. 2. A Coceptual Model for Programmig Laguage Etities The resource flow graph is a graph abstractio of a system which is used to defie the similarity metric at the core of similarity clusterig. It captures typical relatioships betwee routies, variables, ad user-defied types, the buildig blocks of atomic compoets. The relatioships cosidered i this approach are illustrated i the etity-relatioship diagram of Figure 1. 1 m m user-defied type is-part-of-type calls retur-type routie parameter-of-type m actual-parameter uses m sets m m global variable of-type 1 etity 1 relatioship of cardiality 1: Figure 1. A etity-relatioship model for elemets of atomic compoets. This etity-relatioship model defies the structure of a resource flow graph which is a abstractio of the source code. Nodes of this graph stad for the etities ad edges express the relatioships amog etities. I the followig sectio, the resource flow graph is used to defie the similarity metrics. 3. Similarity Metric Approach This sectio presets the similarity clusterig approach. It first gives a overview of the approach. The it discusses the various aspects of similarity which are combied i the approach. This approach is ispired by Schwake s work [Schw91] aimed at detectig subsystems usig a similarity metric betwee routies. The clusterig similarity approach applies the idea of this work to atomic compoets idetificatio by geeralizig this metric, addig iformal iformatio, edge-depedet weights, ad adaptig may of its parameters. The two approaches will be cotrasted i more detail i sectio Approach Overview The similarity clusterig approach groups etities (fuctios, user-defied types, ad global variables) accordig to the proportio of features (etities they access, their ame, the file where they are defied, etc.) they have i commo. The ituitio is that if these features reflect the correct direct ad idirect relatioships betwee these etities, the etities which have the most similar relatioships should belog to the same ADT or ASE. Fuctios, variables, ad types are grouped accordig to the followig algorithm: Place each etity i a group by itself repeat Idetify the most similar groups Combie them util the existig groups are satisfactory remove groups which are ot valid atomic compoets I each iteratio of this algorithm, a similarity metric measures the proportio of features which are shared. The algorithm termiates whe existig groups are satisfactory. I the experimet reported i this paper, groups are cosidered satisfactory whe the most similar groups have a similarity which is below a certai threshold. This threshold is established experimetally o systems where atomic compoets are kow. The similarity metric is costructed of three layers: The similarity betwee two groups of etities which is defied i terms of similarity betwee etities across groups.

3 The similarity betwee two etities which is a weighted sum of various aspects of similarity. Each specific aspect of similarity betwee two etities. The similarity betwee groups of etities used i this paper is defied as the average betwee all pairs of similarities i the two sets: Sim( S 1, S 2 ) = The similarity betwee two etities is the weighted sum of various aspects of similarity. The followig aspects are icluded i the similarity metric for the experimet reported i this paper: Direct relatios Idirect relatios Iformal iformatio Direct relatios are relatios betwee the two etities compared. Idirect relatios are relatios with commo third etities. Iformal iformatio is the iformatio i a program source code which is ot captured by the sematics of programmig laguages, but is used by programmers to commuicate the itet of a program (e.g., commets, idetifier ames, file orgaizatio, etc.) The similarity betwee two etities A ad B is defied as follows (the factors x i are used to adjust the ifluece of the diverse specific similarities): Sim( A, = [ x 1 Sim idirect ( A, + x 2 Sim direct ( A, + x 3 Sim iformal ( A, ] ( x 1 + x 2 + x 2 ) Each aspect is ormalized to obtai values betwee 0 ad 1, so the resultig similarity is also ormalized Direct Relatios Sim( s i, s j ) ( s i S 1, s j S 2 ) size( S 1 ) size( S 2 ) Direct relatios represet immediate coectios betwee two etities ad are geerally more importat tha idirect relatios ad iformal iformatio. Their cotributio to the similarity is computed assim direct ( A, which yields a value betwee 0 ad 1. I terms of the resource flow graph, this is defied as the weighed sum of edges betwee A ad B divided by the weighed sum of all possible edge types betwee A ad B: Sim direct ( A, W ( lik( A, ) = W ( all-liks( odetype( A), odetype( )) where lik (A, deotes the actual liks betwee A ad B, all-liks() deotes all edge-types which are possible i a resource flow graph betwee two give ode types. W ( X) = weight( x) where weight( x) 0 is a x X weightig factor which allows to give certai features more ifluece o the global value of the metric. Weights The weight factors are itroduced to give more ifluece to certai features. The weights are the first factors used to improve the precisio of the approach. Shao Iformatio Cotet Oe weightig factor weight(x) used i this paper is Shao s iformatio cotet [Sha72] from iformatio theory: weight( x) = log( probability( x) ) where probability(x) is replaced by the fractio of all etities that have x i commo. The hypothesis is that rarely used etities are more sigificat tha frequet etities. For example, a error variable which is used everywhere i the system is less distictive tha a variable that is oly used by a small portio of the system. Relatio Type A alterative to Shao s iformatio cotet is to assig fixed edge-weights to the kid of edges betwee etities. For example, whe lookig for a ADT, edges coected to user-defied types are more importat tha call edges, hece should be give more weight. These fixed weights are used i some experimets. I other experimets they were combied with Shao s iformatio cotet by multiplyig the two values Idirect Relatios Idirect relatios capture the proportio of commo features two etities share. I terms of the resource flow graph, these features are eighbors of the etities compared. The similarity aspect for idirect relatios uses the followig formula: Sim idirect ( A, Commo( A, = Commo( A, + d Distict( A, where Commo (A, reflects the umber of commo features of A ad B, Distict (A, reflects the umber of distict features ad d 0 is a parameter which regulates the importace give to distict features. I terms of the resource flow graph, they are defied as follows: Commo( A, = W ( edges-with( A) edges-with( ) Distict( A, = W ( edges-with( A) edges-with( ) The operator deotes the symmetric differece for sets. The term edges-with( returs a set of odes which are coected to B. The exact set of odes depeds o whether the directio of edges is cosidered or ot.

4 3.4. Iformal Iformatio Programmers capture part of the meaig of programs i commets ad i the ame of fuctios, variables, ad types. This helps them ad other programmers fid their way aroud i a program. Aother guide i a program is the file orgaizatio: related fuctios, variables, ad types are ofte put together i oe file. Both of these meas of commuicatio amog programmers are examples of iformal iformatio. Usually, iformal iformatio is igored by reverse egieerig techiques (a otable exceptio is [Bigg89]) which focus o the iformatio kept by a compiler. This sectio discusses how the iformatio cotaied i the ames of program idetifiers ad file orgaizatio ca be relevat to the idetificatio of atomic compoets. It also describes how they are take ito accout by the proposed approach Names of Idetifiers The amig of fuctios, variables, ad types is a importat source of iformatio about a program give to a huma reader. It has bee observed [Bigg89] that eve the author of a program has difficulties i recogizig the purpose of a excerpt from his code oce sigificat idetifier ames have bee replaced by isigificat oes. (e.g., top_stack -> f1). The amig of idetifiers also covey iformatio relevat to the idetificatio of atomic compoets. For example, i oe of the systems ivestigated routies that belog to a abstract data type list had similar ames: list_isert, list_remove, ad list_create. Two amig covetios are widely used for log idetifiers built from may words: separate words with uderscore ( _ ) or start each ew word with a capital letter (e.g., IsertWord). The followig metric based o the umber of commo words betwee two idetifiers exploits these covetios: words( X) words( Y ) Sim words ( X, Y ) = words( X) words( Y ) Whe these covetios are ot used, idetifiers are frequetly costructed usig a commo prefix or postfix. I this case the followig metric is used: Sim suffix ( X, Y ) = 1 X = Y prefix( X, Y ) + postfix( X, Y ) 1 + prefix( X, Y ) + postfix( X, Y ) X Y where prefix ad postfix are the legths of the commo pre- ad postfix of their two argumets, if the legth is loger tha three characters; otherwise it is zero Orgaizatio of Files The divisio of a program ito files also coveys some iformatio about the meaig of a program. Related fuctios ad variables are ofte put i the same file or i files with a commo substrig i their ame (e.g., clietdb ad cliet-service). The previous metric for idetifier ame similarity based o pre- ad postfixes is used to compare file ames without extesios (i.e., oly file i file.c or file.h) Sim fileame ( X, Y ) = Sim suffix ( fame( X), fame( Y )) 4. Related Research This sectio presets a brief overview of the related research i reverse egieerig ad similarity metrics Detectio of ADTs ad ASEs or Objects There is a rich literature o the detectio of ADTs ad ASEs or object istaces from systems writte i procedural laguages (usually C). May techiques [Oga94, Gira97a, Yeh95, Caf93a, Caf94] that aim at ADT recovery focus o the relatios betwee types ad the parameters ad the retur type of a fuctio (called fuctio sigature). These relatios are usually represeted by edges i a graph where the odes correspod to fuctios ad user-defied data types of a system. The basic idea of these techiques is that the set of coected compoets of this graph form the set of ADT cadidates. However, these cadidates are ofte too large, because of fuctios which examie or modify structures of may types. As a result, these ADT cadidates ofte combie ad thereby hide multiple correct referece ADTs. Each of the followig techiques proposes a differet heuristic to avoid these large cadidates by imposig additioal coditios o the coected compoets: Same Module heuristic [Gira97a]: It groups oly those routies, global variables, ad types together that are declared i the same file. Part Type heuristic [Oga94]. It filters out types i a parameter list that are part of aother type i the list. Iteral Access heuristic [Yeh95]: It filters out routies which do ot access iteral parts of record types that are part of a ADT. Eumeratio & Domiace heuristic [Caf94]: It filters out eumeratios ad subrages from the types cosidered to form a ADT whe the ADT is too large. It also uses the domiace relatio o the call graph to idetify fuctios which are called oly from a ADT i order to package them with the ADT.

5 Our similarity clusterig cosiders the same relatios betwee fuctios ad types, but it also takes ito accout the relatios with the cotext of etities. Like the Same Module heuristic, similarity clusterig cosiders the file where types ad fuctios are defied, but the ifluece of the file orgaizatio o the metric ca be lowered whe it is ot a importat factor. Like Eumeratio ad Domiace heuristic, similarity clusterig cosiders the call relatioship betwee routies. Yeh et al. [Yeh95] ad Cafora et al. [Caf93] idetify ASEs or object istaces based o the set ad use relatioship betwee fuctios ad global variables. These relatios are usually represeted by edges i a graph where the odes correspod to fuctios ad global variables. These approaches ca be summarized as follows: Delta IC (a adaptatio of [Caf93b]) It geerates potetial ASEs usig a fixed patter, the evaluates the improvemet o a coectivity metric due to the itroductio of a potetial cadidate. If the improvemet is good eough, the the cadidate is kept, otherwise it is rejected. Object Istace heuristic [Yeh95]: It computes the coected compoets of this graph ad cosiders them as ASE cadidates. Our similarity clusterig differs from these techiques i that it takes ito accout how frequetly a variable is used while creatig cadidates. This addresses oe of the two causes of massive compoets reported by Yeh et al. [Yeh95] of exteral variables or structures used throughout the system. Givig more weight to variables used ifrequetly ca alleviate this problem Schwake s Approach I [Schw91], Schwake defies a similarity metric used to group fuctios ito modules. This approach differs from previous approaches i that it geerates cadidates usig a metric which provides a more refied view o the relatioships betwee program etities. The previous approaches compute coected compoets or other fixed patters i a graph. I cotrast, whether these patters are preset or ot, Schwake s approach deals with cotiuous values which reflect the degrees of similarity. This is computed as Sim idirect give i sectio 3.3. The similarity clusterig approach exploits these uaces, but exteds the metric i the directios summarized i Table Similarity Similarity metrics have bee researched extesively by the AI commuity, i particular the case-based reasoig commuity. May aspects of this research focus o specific eeds of this commuity, for example Aamodt [Aamo94] records kowledge about the field i order to provide explaatios. However, some refereces like Richter [Rich92] preset a good overview of the basic ideas ad itroduce learig approaches which ca be applied to discover the appropriate similarity. A systematic search for the best possible similarity metric is beyod the objective of this research. However, it is future work to idetify what could be applied from related work o similarity metrics. Table 1: Compariso with Schwake s Approach Schwake s Approach 5. Experimet Setup I order to evaluate our similarity clusterig approach, it was applied to three medium-size programs, ad the results were the compared to the atomic compoets idetified by software egieers. The compoets idetified by the approach are called the cadidate compoets. The atomic compoets idetified by software egieers will be called referece compoets. This sectio summarizes the experimetal setup ad the aalysis method used Systems Studied Article s Approach Domai routies routies user-defied types global variables Weights Shao ifo. Shao ifo. edges weight which reflect the type of relatio betwee etities features iformal ifo. usage of o-local ames ot used type relatios usage relatios call relatios tokes i idetifiers pre- ad postfix i idetifiers file direct liks values = 0 or 1 cotiuous values betwee 0 ad 1 similarity betwee groups maximum similarity betwee elemets average similarity betwee elemets The aalyses described above were applied to three medium size C programs (see Table 2 for their characteristics). Aero is a X widow system-based simulator for rigid body systems [Kell95], bash is a Uix shell, ad CVS is a tool for cotrollig cocurret software developmet.

6 System Name Versio 5.2. Huma Aalysts Table 2. Systems Studied Lies of Code # User Defied Types # Global Variables Five software egieers were give the task of idetifyig atomic compoets i each system. These systems were ukow to them. There was o overlap of their work. They eeded about 20 hours for each system to gather the atomic compoets of the respective systems. Details cocerig the guidelies give to the software egieers, their experiece,ad how the task was divided amog them are described i [Gira97b]. Table 3 shows the respective umbers of all forms of atomic compoets (abstract data types, abstract state ecapsulatio, hybrid atomic compoets) that were idetified by the group of software egieers for each studied system. The variatio i experiece ad the umber of people workig o each system prevet compariso of differet techiques across systems util cross-validatio ad future studies characterize the importace of these factors. However, prelimiary ispectio of the idetified atomic compoets foud o aberrat compoets. Furthermore, the fact that oe of the software egieers kew the automatic aalyses to be applied to the systems, preveted bias toward a specific techique Compariso of Cadidate ad Referece Compoets # User Defied Routies aero Kloc bash Kloc cvs Kloc Table 3. Referece Atomic Compoets. System #ADT #ASE #Hybrid Aero Bash CVS This subsectio explais how imperfect matches betwee cadidate ad referece compoets are compared ad classified. Cadidate compoets Cs ad referece compoets Rs are compared usig a approximate matchig to accommodate the fact that the distributio of fuctios, global variables, ad types ito atomic compoets is sometimes subjective. We treat oe compoet S as part of aother compoet T (deoted by S << T) if at least 70 percet of the elemets of S are also i T. Based o this approximatio, the geerated cadidates are classified ito 3 categories accordig to their usefuless to a software egieer lookig for atomic compoets: Good whe the match betwee a cadidate C ad a referece R is close (i.e., C << R ad R << C). Matches of this type require a quick verificatio i order to idetify the few elemets which should be removed or added to the atomic compoet. Ok whe the relatioship holds oly i oe directio for cadidates C i ad refereces R i : - C i << R, but ot R << C i (i > 0) - R i << C, but ot C <<R i (i > 0) Partial matches of this type require more attetio to split, combie, or refie a compoet. Bad cadidate compoets are ot close eough to the referece compoets to guide the software egieer s work Accuracy I order to idicate the quality of imperfect matches of cadidate ad referece compoets, a accuracy factor has bee associated with each match. The accuracy betwee a cadidate C ad a referece R is computed by the followig formula: C R accuracy( C, R) = C R For matches betwee more tha two compoets (~1 ad 1~) the uio of all elemets of the compoets is used to compute the accuracy. The accuracy is ot defied for ~m matches, because the m refereces are ot always uique. 6. Results This sectio compares the atomic compoets recovered by similarity clusterig with those recovered by other techiques. The it discusses the impact of false positives. Bechmark Results. The case study [Gira97b] applied the followig techiques to recover atomic compoets o the three systems described here ad used the same evaluatio setup: same module [Gira97a] part type [Oga94] iteral access (a adapted versio of [Yeh95] that combies results of Yeh et al. s iteral access ad object istace heuristic). delta IC (a adaptatio of [Caf93b])

7 Schwake (a miimal adaptatio of [Schw91]) which is modified oly to cosider variables ad types as well as fuctios. All other parameters remai as described i [Schw91]. The umber of recovered ADTs ad ASEs for each system usig the above techiques are reported i the case study [Gira97b]. There, the results are aalyzed i more detail. I Table 4 we cotrast these results with the outcome of our ew aalyzig techique (last lie of the table). The table etries correspod to the umber of compoets (good + ok) recovered by each techique multiplied by their accuracy. It ca be observed that similarity clusterig idetifies cosiderably more ADTs ad ASEs tha the other techiques except for the ASEs of CVS ad Aero. For approaches that are ot able to detect a certai kid of atomic compoet, the respective etries i Table 4 are left empty. Because of lack of space we leave out the results for hybrid compoets. They cofirm the results for ADT ad ASE detectio. Table 4: Recovery Summary Techique aero bash cvs adt ase adt ase adt ase Same Module Part Type Iteral Access Delta IC Schwake Similarity Clusterig However, idetifyig these additioal compoets does come with a high price: with the curret implemetatio, the umber of false positives (bad category) is sigificatly larger as Table 5 shows. Table 5: False Positive Atomic Compoets Techique aero bash cvs Same Module Iteral Access Part Type Delta IC Schwake Similarity clusterig I gatherig the results preseted here, we used 0.15 (aero & cvs) ad 0.25 (bash) as thresholds for the clusterig algorithm outlied i sectio 3.1. I practice, whe the umber of cadidates is too large, oe ca decide to modify the threshold to obtai less cadidates or to use the other methods. For example, whe the threshold for bash is raised from 0.3 to 0.4, the umber of cadidates drops from 209 to 135 cadidates, but the umber of good matches drops from 6 to 2. It is a topic of further research to fid a way to reduce the umber of false positives. 7. Coclusios I this article, we preseted a ew approach to extract atomic compoets (abstract data types ad abstract state ecapsulatios i this paper) from source code. At the core of our work is a clusterig method based o a similarity metric betwee elemetary program etities (e.g., routies, types, ad variables) ispired by the work of Schwake [Schw91]. I cotrast to the origial approach of Schwake, we geeralized the similarity metric to take ito accout edge-depedet weights ad iformal iformatio extracted from idetifier ad file ames. Our experimetal results show that these extesios improve the quality ad quatity of atomic compoets foud. We evaluated the performace of this approach i the cotext where it is importat to recover as may of these as possible. We compared the compoets idetified by a group of software egieers o three C systems (30-38 Kloc) to the compoets idetified by similarity clusterig ad five other published approaches. I most cases, our similarity clusterig approach idetifies more atomic compoets tha the other approaches. However, the goal of recogizig atomic compoets of the architecture of software systems is oly partially solved. Though givig better results tha other methods proposed i the literature, our clusterig method recovers oly a portio of them ad would beefit from beig combied with the results of other approaches. Also, the price to pay for these results is a high umber of false positives. I practice, this approach would be applied to a system to suggest a list of cadidates which are to be reviewed by software egieers. Reviewig cadidates is much faster tha lookig for the atomic compoets i the source code. Fidig atomic cadidates i the code took o average 20 hours for each of the systems studied. Whe there are too may cadidates to review for the time available, the practitioer ca adjust the threshold to reduce the umber of cadidates or he might select to use a alterative approach. I both cases, the case study reported i this paper could help him amke a iformed decisio, beig aware of the trade-offs ivolved. I summary, despite its drawbacks, the similarity clusterig approach is a useful tool for idetifyig atomic compoets. It is a good complemet to existig approaches ad ca be combied with those.

8 Future work Develop meas for reducig the umber of false positives i cadidates. Possibilities ivestigated iclude statistical filterig of cadidates ad better parametrizatio. Study the relatios betwee the various parameters of the approach tryig to fid out which oes are systemdepedet. Develop a systematic process to establish the parameters ad report experimetal results. Explore how the various approaches for idetifyig atomic compoets ca be combied. Ackowledgmet We would like to thak Jea-Marc DeBaud ad Erhard Plödereder for their support to this project ad their isightful iputs to this research. We would like to express special thaks to Joachim Bayer, Hiltrud Betz, Kristia Ludqvist, Mia Mäkäräie, ad Klaus Schmid for their maual aalysis of the three subject systems used i our compariso. Refereces [Aamo94] [Bigg89] [Caf93a] A. Aamodt. Topic i Case-Based Reasoig, chapter Explaatio-Based Case-Based Reasoig, pages Spriger Verlag, T. J. Biggerstaff. Desig recovery for maiteace ad reuse. IEEE Computer, 22:36 49, July G. Cafora, A. Cimitile, ad M. Muro. A reverse egieerig method for idetifyig reusable abstract data type. I Workig Coferece o Reverse Egieerig, pages May [Caf93b] G. Cafora, A. Cimitile, M. Muro, ad C. J. Taylor. Extractig abstract data type from C programs: A case study. I Iteratioal Coferece o Software Maiteace, pages September [Caf94] G. Cafora, A. Cimitile, M.Tortorella, ad M. Muro. A precise method for idetifyig reusable abstract data types i code. I Iteratioal Coferece o Software Maiteace, pages September [Ghez91] [Gira97a] [Gira97b] [Kell95] [Oga94] [Rich92] [Schw91] [Sha72] [Somm92] [Yeh95] C. Ghezzi, M. Jazayeri, ad D. Madrioli. Fudametal Software Egieerig. Pretice Hall Iteratioal, J.F Girard ad R. Koschke. Fidig compoets i a hierarchy of modules: a step towards architectural uderstadig. I Iteratioal Coferece o Software Maiteace, J.F Girard, R. Koschke, ad G. Schied. Compariso of abstract data type ad abstract state ecapsulatio detectio techiques for architectural uderstadig. I Fourth Workig Coferece o Reverse Egieerig, October H. Keller, H. Stolz, A. Ziegler, ad T. Bräul. Virtual mechaics simulatio ad aimatio of rigid body systems with aero. Simulatio for Uderstadig, 65(1):74 79, July R.M. Ogado, S.S. Yau, ad N. Wilde. A object fider for program structure uderstadig i software maiteace. Joural of Software Maiteace, 6(5):261 83, September-October Richter. Classificatio ad learig of similarity measures. I Aual Coferece of the Germa Society for Classificatio, umber 16th. Spriger Verlag, R. W. Schwake. A itelliget tool for re-egieerig software modularity. I Iteratioal Coferece o Software Egieerig, pages 83 92, May C. E. Shao. The mathematical theory of commuicatio. Urbaa: Uiv. of Ill. Press, ISBN I. Sommerville. Software Egieerig. Addiso Wesley, fourth editio, A.S. Yeh, D.Harris, ad H. Reubestei. Recoverig abstract data types ad object istaces from a covetioal procedural laguage. I Secod Workig Coferece o Reverse Egieerig, pages , Los Alamitos, Califoria, July 1995.