Clustering Analysis for Object Formation in Software Modeling

Transcription

1 Clustering Analysis or Object Formation in Sotware Modeling Dusan Sormaz, Danyu You, and Arkopaul Sarkar Department o Industrial and Systems Engineering, Ohio Uniersity, Athens, OH, USA Institute or Corrosion and Multiphase Technologies, Ohio Uniersity, Athens, OH, USA Abstract - Object-oriented sotware modeling (OOM) is a widely used approach to proide structured way o designing sotware architecture. The object in OOM is a group o unctions and attributes which perorm related unctionalities. The identiication o objects is challenging in some application domains where unctionalities are diicult to be partitioned/grouped, or instance, chemical calculations. This paper conducts the clustering analysis to partition those unctions and attributes and orm preliminary objects or OOM. The objectie o clustering algorithm is to minimize the number o unctions and attributes shared by more than one object. In this study, a unction attribute matrix is irst deeloped to represent the requirement o attributes or each unction. And then a clustering algorithm is proposed to continuously restructure the unction attribute matrix until the termination conditions are met or no more improement can be ound. The experimentation is carried based on the corrosion prediction sotware which contains up to 00 nested unctions and attributes. And the results show that the promising approach can capture the homogeneous unctions and attributes and orm preliminary objects. Keywords: sotware clustering, legacy sotware, object oriented modeling Introduction Object-oriented sotware modeling (OOM) is a matured sotware design unction and widely applied in the early phase o sotware deelopment []. Clustering algorithms are commonly unctions o discoering intrinsic patterns rom a collection o raw data. It has been applied in many domains such as group technology, cellular manuacturing, organizational behaior and others. This paper intends to apply the clustering technology on reerence rom unctions to ariables, and to reeal potential combination pattern o them, which then is used to ind objects. The rest o this paper is organized as ollowing: Section describes the background o the current problem. Section reiews the deelopment in the sotware modeling and the clustering technology. Section explains the construction o Functions-Variables matrix, the clustering algorithm, the adjustments to result and the creation o classes. Section 5 shows results o applying clustering analysis on an existing sotware application. At last, in section 6, the conclusion is drawn and the reerence papers are listed. Problem Description This paper ocuses on identiication o potential objects, which is the preparation o OOM; in existing sotware applications. In our case, the target system is a sotware application or predicting corrosion rate o oil pipeline []. Since it was written many years ago when OO eatures were not supported by Fortran 77 language, it is not an OO designed application, which means a large portion o its unctions and ariables were deined globally to simpliy passing arguments through unctions, but its unctional modules are coupled tightly. An obious shortcoming o such structure is: wheneer any modiication is introduced into this system, corresponding global inluences should be considered. It makes deelopment and maintenance become increasingly harder as the growth o application size. To sole that problem, a loosen-coupled structure should be built to replace that out-dated structures. As OOM is a ery common and matured unction to construct lexible sotware structure, and OO mannered programming starts being supported by Fortran 90, it is applied to guide the process o reorganization. In order to sole the problem, well-known unction o clustering is it s applies rom is chosen and the goal has been set to explore its perormance in a new domain. Using OOM to build system models, the initial stage is deining system objects and understanding relationship between them in a high enough leel. Usually, deelopers hae complete reedom to create necessary objects as long as they are consistent with customer s requirements. But in this case, in order keep existing modules, which are well unctioned, OOM has to start rom the lowest leer the leel o codes, and objects needs to be extracted rom codes. In another words, the task is to obsere potential groups o interrelated unctions and ariables rom current codes to build objects. This is the opposite o the early process o OOM; howeer, it is what clustering technology is designed or inding groups in a collection o unstructured data.

2 Preious Work. Sotware Reorganization Sotware reorganization is quite common in sotware engineering. Studies o sotware engineering [] reeal that a great amount o code changes are requested in the later stage o deelopment and een ater deelopment is inished. Implementation o those changes is proed not only costly and time-consuming, but also negatiely impacts existing system architecture, especially when the existing structure is not well designed to accommodate changes. Under some serious situation, the existing design een needs to be reorganized to upgrade to a better structure. OOM, because o its successul application in sotware engineering, becomes the most common technology being used in process sotware design and redesign. The core concept o OOM is the object []which is a group o unctionally related unctions / unctions and attributes / ariables, called members. Other concepts, such as inheritance, interace and polymorphism, help to construct a lexible network o objects. Inside objects, members are coupled tightly; outside objects, models are loosen-coupled [7].. Clustering Algorithms Clustering algorithm is a unction in obsering structures o data so that interrelated members can be organized into groups [8]. It has been used in dierse domains, such as network optimization [], inormation retrieal [], market research [9], and quality control []. Methodology The clustering analysis o the existing sotware structure is perormed through seeral steps as shown in igure. First, a unction-ariable matrix is built or the existing sotware package. For clustering, King s ROC algorithm [5] is applied. Then, adjustments are implemented to simpliy matrix. By diiding the matrix into segments and treating each segment as a class, the unction-ariable matrix is changed into a class-unction matrix. Through analysis against the matrix, unctions are distributed into the class haing the strongest connection to them. At the end, the logic o cross reerence between classes is established ocus the existing relations.. Function-Variable Matrix Function-ariable matrix is a binary matrix constructed to explore the relationship between unction untions and ariables, which can be expressed by equation (): Start Deine Method Variable Matrix Run clustering algorithm Simpliy Matrix Build Class-Method Matrix Create preliminary classes End Figure : The procedure o methodology Where, r is the row o matrix, equals to number o unctions; c is the column o matrix, and equals number o ariables. X 0 or () Where, ie [, r] and je[, c]. I Xi, j=, it means unction with index i uses ariable with index j; i Xi, j=0, there is no connection between unction i and a ariable j. Following is an example o the unction ariable matrix, which is or an application haing 8 ariables and unctions. So the matrix is a two dimensional array with rows and 8 columns. In that matrix, X, =, which means ariable is used in unction Figure. An Example o unction-ariable Matrix Another example gies a demonstration o how to build such a matrix rom codes. A segment o codes is taken rom MULTICORP and shown in Figure, where unctions names and global ariables are marked by rectangles and ellipses separately (repeatedly used ariables are only marked once). There are dierent global ariables and unctions. Using the method discussed in this section, the corresponding unction-ariable matrix haing columns and rows is built and show in igure X matrix( r c) ()

3 () Sort columns basing on their scores rom low to high, () Sort rows by the same way, () Ater each sorting, compare with the latest matrix with the preious one, i there is no obious change ater sorting, stop algorithm; otherwise, go to (), continue the loop. For example, i applying preious steps to matrix in igure, ollowing result (igure ) should be generated. Figure : An example o building unction-ariable matrix rom codes.. Run Algorithm King s POC algorithm is applied a unction ariable matrix connected ocus the sotware. To apply the algorithm or each unction and ariable, a score is assigned by using ollowing equations. Each row and column o the matrix is gien a weight actor equal to i- or j- (or example, third row has weight = 8). Then or each method and ariable, a score is assigned by using ollowing equations. Score o the unction is, c Si X * W () j Similarly, score o the ariable is, S j= r i X * W i j () For example, using equation (), in igure, the score or unction (the ourth row) is = =7. The clustering algorithm applied by this paper. Using score or rows and columns, King s ROC algorithm is applied on the ollowing sequence: Figure : Result o applying the algorithm against the matrix in igure. Adjustments I there is a complete separation o unctions and ariable result o clustering, igure 5 would be an ideal combination o ariables and unctions. Such a result, howeer, cannot be expected in a real case is usually not obtained Figure 5: An example o ideal result For a realistic matrix, which does not hae such a clean pattern, such as the result shown in igure 5, some adjustments are necessary or its simpliication. Rules are deeloped or adjustments and described as ollowing: Rule : Separate global ariables I r i X r * p, [, c] 5 6 j, (0,] 7 8 p, in our case, p = 0%. This Rule indicates, a ariable will keep staying as a global one and be taken out o the matrix i it is used by a relatiely large collection o unctions, say 0% o total unctions. An extreme situation is shown in igure 6, where ariable 5 is used by eery unction. So V5 should be global and not be considered in this matrix. () Calculate score or each row and each column,

4 Figure 6: An example application o Rule The matrix needs to be diided into diisions horizontally, and each o them becomes a class. The basic rule o drawing diision is trying to put as much as possible usages (represented by cells with number ) o the same ariable into one area. One possible plan o diision is gien by red lines in igure 9. Rule : Combine ariables (columns) with the same unctions callers. I there are existing n columns, or, is true, then keep only one column o them, and remoe the rest. An example is shown in igure 7. Figure 7: An example application o Rule Rule : Combine unctions which has the same set o ariables. I there are existing n rows, or, is true, then keep only one row, and remoe the rest. An example is shown in igure 8. Figure 9: An example o possible diision o a matrix.. Assign Functions to Classes Gien diisions rom igure 9, another orm o matrix, the unction-class matrix, is deeloped. The unction-class matrix shown is similar to the unction-ariable matrix built in Section. except o ollowing two dierences: () The irst row contains name o preliminary classes (excluding irst cell to the let); () Cells contain integer numbers instead o Boolean alue to indicate how many ariables a unction uses in the class. An example is shown in igure 0, which is generated rom the matrix in igure6. Figure 8: An example application o Rule Rule : Eliminate ariables which are used in only one unction. Ater applying those rules, it may be necessary to run POC algorithm once again to obtain additional clusters.. Create Classes Ater the clustering completed, the inal matrix seres the purpose o deining the classless. Class creation is perormed in three steps: indentiy diisions, assign methods to classes, setup cross reerences between the classes as shown in this section... Identiy Diisions Classes are ormed by identiying diisions as shown Figure 6 gies an example o the matrix that might be ormed ater preious steps. Figure 0 Matrix improement As shown in igure 0-(a), some unctions are shared by classes, but, in OOM, one speciic unction usually only belong to one class. Other classes that need to call that unction only need to keep an instance o that class. A unction should be put into the class that has the strongest connection with it. Assuming that, the more one unction is used in one class, the stronger their connection is. In igure 0, the irst maximum number o each row is marked with green color to tell which class uses a unction the most. Then, rows are swapped to make green cells in the same column stay as continuous as possible. Swapped matrix is shown in igure 0-(a). In column o each class, there is a strip o continuous green cells (circled by red rectangle), which stands or a collection o unctions that are strongly depended by the

5 class. Thus, those unctions are assigned to their depending class... Set up cross reerences between classes The last step is to construct a bridge between classes so that a unction is able to be called by other classes. This bridge is the reerence. In OOM, a reerence is an instance o a class. In order to highlight reerences between classes, the matrix in igure 0-(b) is simpliied into a new matrix and shown in igure. Based on table, a solution or calling unctions between classes is proposed in igure. Figure : calling methods between classes Figure : Function class matrix. In igure, a green rectangle is considered as a class; integer number inside a cell means how many ariables a class uses unctions orm another one. Speciically, or Xi, j = N, Xi, j is the alue o cell in row i and column j, i>0, j>0, N 0, () I i > j, it means class i calls unction rom class j N times; () I i<j, it means class j calls unction rom class i N times. In igure, or example, X, =, which means Class uses unctions rom Class two times; and X, = means class uses unctions rom Class one time. The matrix can be interpreted by table to indicate i a class needs an instance o another one. Table Establishing reerences between classes Class Name Reerence to Class Reerence to Class Reerence to Class Class -- No Yes Class Yes -- Yes Class No Yes -- 5 Case Study Methodology discussed in section is applied on a collection o unctions and ariables rom a real sotware application, Multicorp/Corrsim, originally implemented in Fortran 77, and results are shown. AS a result the application has been conerted into Fortran 90, which supports abstract data types and allows application o object-oriented modeling. 5. Build unction-ariable matrix Analysis o the existing application reeals that there are unctions and more 90 global ariables. Related unctionariable matrix is built and shown in Figure. 5. Apply clustering algorithm and adjust Ater the irst clustering, matrix is changed and shown in igure, which is not excellent but expected. Though, there is some clustering achieed, the structure o the code requires urther analysis. Through adjustments discussed in section., the matrix is simpliied to ariables and unctions and shown in igure 5. Figure : Initial unction-ariable matrix

6 Figure : Preliminary result o running algorithm Figure 5: Diisions o matrix 5. Identiy classes In igure 5, our diisions are drawn ia unction discussed in section.. and ariables are grouped into the ollowing classes: Class: RK NCHEM DHHC IANODE CBULK IHUPOHE IJOTO NY POROSITYI ISANODE IST NHS NCO Class: Z Class: NHC IELCHHAC NHC GIBBSHPLS TIME NHPLS FARADAY ISTST FLUXI Figure 6 Mark maximum in each row o Class-unction Matrix By swapping rows in the matrix, green cells in the same column are connected together. Each strip o green cell is a collection o unctions that should belong to the same class. TPAS NYFILM DIFF KAPPA VISC PORTOR TORTUOSITY DIFFM TEMP IPAS Class: IMIGR NUMANODE DELTAX DELTAY PORTORI DIFFI DYCV AMX FEXCOK DXCV NX NFL UMOB DT IPIT Then, the matrix is modiied into a class-unction matrix, which is shown in igure 6-(a). The maximum number o each row is marked as green to show connections between unctions and classes. Figure 7 Matrix with classiied unctions urther simpliied Using unction discussed in..., continuous green cells and cells inside red rectangle are merged into single cells, a

7 simple and clean matrix about connection between classes is ormed. Based on structure indicated by this igure, a possible solution or building our classes are proposed as ollowing. Figure 8: One possible implementation o collaboration between classes 6 Conclusions and uture studies The results shown in section 5 demonstrate that, in the process o redesigning the structure o an existing sotware application, it is possible to draw a rough picture o potential classes and their interrelation by using clustering algorithms, without ully understanding the logic structure o that application. Although this unction does not guarantee the correctness and reasonability o the results, it at least can serer as preprocess o OOM to help discoer some patterns inside codes. Future work will be related to how to use results rom this paper to assist semantic analysis, and also to distinguish between reading and writing ariables. Implementation o the improed ersion o the sotware based on the indings is under way. [7] Li W. and Henry S., 99, OO Metrics that Predict Maintainability, Journal o Systems and Sotware, (), -. [8] Wu Z., and Leahy R., 99, An Optimal Graph Theoretic Approach to Data Clustering: Theory and Its Application to Image Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 5, 0. [9] Punj G., and Stewart D.W., 98, Cluster analysis in marketing research: reiew and suggestions or application, Journal o Marketing Research, 0, 8. [0] Lew M.S., Sebe N., Djeraba C., and Jain R., 006, Content-based multimedia inormation retrieal: state o the art and challenges, ACM Transactions on Multimedia Computing, Communication, and Applications,, 9. [] Zarandia M. H. F., and Alaeddinib A., 00, A general uzzy-statistical clustering approach or estimating the time o change in ariable sampling control charts, Inormation Sciences, 80, 0 0. [] Deng Y., and Wang F., 006, Optimal Clustering Size O The Small File Access In Network Attached Storage Deice, Parallel Processing Letters, 6 (), Reerences [] S. Nesic, Jiyong Cai, Shihuai Wang, Ying Xiao and Dong Liu, Ohio Uniersity Multiphase Flow and Corrosion Prediction Sotware Package MULTICORP V.0, Ohio Uniersity(00). [] Chandra K. S., 007, Identiying Sotware Patterns Approaches to Cognitie Modeling, The ICFAI Journal o Computer Sciences, 56 (), [] Aksit M., 996, Composition and Separation o Concerns in the Object-Oriented Model, ACM Computing Sureys, Vol. 8A, No.. [] Sia Balan R. V., Punithaalli M., 00, Sotware Architecture, Scenario and Patterns, IJCSI International Journal o Computer Science Issues, 7 (5), 8- [5] Kamrani A. K., 998, Group Technology and Cellular Manuacturing: Methodologies and Applications, Taylor & Francis [6] Landauer T. K., 995, The Trouble with Computers: Useulness, Usability and Productiity., MIT Press., Cambridge.