In this paper we refine the classical co-change to the addition of
method calls. We use this concept to find usage patterns and to
identify cross-cutting concerns for ArgoUML

Breu, Silvia

Lindig, Christian

Livshits, Benjamin

Zimmermann, Thomas

Dagstuhl Research Online Publication Server

Mining Additions of Method Calls in ArgoUMLThomas Zimmermann1 Silvia Breu2 Christian Lindig1 Benjamin Livshits31 Dept. of Computer ScienceSaarland UniversitySaarbru¨cken, Germany{tz, cl}@st.cs.uni-sb.de2 University of CambridgeComputer LaboratoryCambridge, UKsilvia@ieee.org3 Dept. of Computer ScienceStanford UniversityStanford, USAlivshits@cs.stanford.eduABSTRACTIn this paper we refine the classical co-change to the addition ofmethod calls. We use this concept to find usage patterns and toidentify cross-cutting concerns for ArgoUML.Categories and Subject DescriptorsD.2.7 [Software Engineering]: Distribution, Maintenance, andEnhancement—version control; D.2.9 [Management]: Softwareconfiguration managementGeneral TermsManagement, Measurement1. INTRODUCTIONOne of the most frequently used techniques for mining versionarchives is co-change. We specialize this concept to the additionof method calls:Two method calls that are added together in thesame transaction, are related to each other.We use the concept of co-additions for the following two tasks:• Find usage patterns, such as “the methods containsNodeand containsEdge are frequently called together.”• Identify cross-cutting concerns, such as “the first statementof every method calls the info method to log the methodname.”In Section 2 we will describe our input data and the tools we used;we present our results for usage patterns in Section 3 and for cross-cutting concerns in Section 4.2. INPUT DATA AND TOOLSWe applied our mining techniques to the ArgoUML repository thatwas supplied for the MSR challenge [4]. We restricted our analysisto the src new directory that contains the actual source code ofArgoUML. All data was collected with an extended version of theeROSE plug-in [2] for the ECLIPSE environment. For mining, weused SQL queries and the Xelopes data mining library [5].To reconstruct transactions we use the sliding window approachwith a window size of 200 seconds. For each transaction we com-pute the set of newly added method calls. For this we compare theCopyright is held by the author/owner.MSR’06, May 22–23, 2006, Shanghai, China.ACM 1-59593-085-X/06/0005.Pattern Countlocalize(2) addField(2) 57localize(1) lookupIcon(1) 45addCaption(4) addField(4) 43addButton(1) lookupIcon(1) 41localize(1) addField(2) 28findFigsForMember(1) findType(1) 23addModelEventListener(2) removeModelEventListener(2) 19addModelEventListener(3) removeModelEventListener(3) 13addFocusListener(1) addKeyListener(1) 12hasMoreElements(0) nextElement(0) 12error(2) debug(1) 11addSeperator(0) addField(2) 10info(1) isInfoEnabled(0) 10max(2) isDisplayed(0) 9containsNode(1) containsEdge(1) 8Table 1: Usage patterns for ArgoUML.total set of method calls from the actual and the previous transac-tion. The total set of method calls is computed for each transactionby traversing the abstract syntax trees of all affected files.For a call expression c1().c2(). . . . cn() we only take the finalmethod call cn() into account. Since we only analyze one file at atime, the full signature for method cn isn’t available. Instead, weaugment it with the number of parameters, as shown in Table 1. An-alyzing single files rather than complete snapshots makes our pre-processing cheap, as well as platform- and compiler-independent.3. MINING USAGE PATTERNSOur approach is based on an observation: Method calls that areadded to source code simultaneously often represent a pattern. Toidentify such patterns, we performed frequent pattern mining onthe set of added method calls.We focused our analysis on intra-procedural patterns: patternsthat occur within a single method. In terms of mining this meansthat we do not use complete transactions as input but group transac-tions by the method in which a call was added. Furthermore, we ig-nored calls to frequently used JAVA methods, such as iterator,hasNext, and toString, since patterns involving these meth-ods are well-known.Table 1 shows the patterns we mined, sorted by decreasing fre-quency. Actual usage patterns are printed in boldface, thus the pre-cision is 40%. Below we discuss a few examples.• addModelEventListener,removeModelEventListenerThis pattern is used when elements are changed. First, thelistener is removed for the old element, then the elementis changed, and finally the listener is added for the newelement.Dagstuhl Seminar Proceedings 06302Aspects For Legacy Applicationshttp://drops.dagstuhl.de/opus/volltexte/2007/886if (Model.getFacade().isAElement(target)) {Model.getPump().removeModelEventListener(this, target);}target = t;if (Model.getFacade().isAElement(target)) {Model.getPump().addModelEventListener(this, target, "name");}• addFocusListener, addKeyListenerThis pattern indicates a relationship between the focus and akey listener: A user may enter text only to graphical elementsthat are in focus.• isInfoEnabled, infoSometimes the return value of isInfoEnabled is checkedbefore the info method is called.if (LOG.isInfoEnabled()) {LOG.info("Removing feature " + feature);}• containsNode, containsEdgeThese two methods are frequently called with the same argu-ments to check whether an edge is valid; if not, an error islogged.if (!containsNode(destModelElement)&& !containsEdge(destModelElement)) {LOG.error("some message");return false;}4. MINING CROSS-CUTTING CONCERNSPrograms can be modularized in only one way at a time. Aspect-oriented programming (AOP) remedies this by factoring out as-pects and weaving them back in a separate processing step. Forexisting projects to benefit from AOP, these cross-cutting concernsmust be identified first. This task is called aspect mining.Our hypothesis is that not all cross-cutting concerns exist fromthe beginning, but some emerge over time. By analyzing where de-velopers add code to a program, we can identify cross-cutting con-cerns. Our approach searches transactions for sets of locations Lwhere at each location calls to a set of methodsM have been added.In other words: The calls to methods M are spread throughoutsource code locations L. We call such a pair (M,L) an aspect can-didate. In order to identify aspect candidates that actually cross-cuta considerable part of a program, we ignore all candidates (M,L)where |L| < 7 or |M | · |L| < 20. This means that each aspect can-didate has to cross-cut at least 7 locations, and it has to comprise atleast 3 method calls that got added.For ArgoUML we identified 230 aspect candidates in 73 outof 6,286 transactions. Below we discuss a few examples.Logging. We observed that the transaction with the log message“Replaced deprecated log4j Category with Logger.” insertedseveral calls to methods debug, error, and warn. Thelast two methods turned out to be false positives. However,for debug we found several cross-cutting calls that loggedthe method names as shown in the following example:public void doAction(int oldStep) {LOG.debug("doAction " + oldStep);...}This logging could have easily been realized with an aspect.Illegal arguments. The transaction with the log message “Madethe methods look a little more alike. Collected the numer-ous IllegalArgument calls in methods. [. . . ]” inserted manycross-cutting calls to illegalArgument or one of itsvariants. These calls are always last in the method body:public String getValueOfTag(Object handle) {if (handle instanceof MTaggedValue) {return ((MTaggedValue) handle).getValue();}return illegalArgumentString(handle);}In this case the method illegalArgumentStringthrows an IllegalArgumentException and returns anull object. Most of the 262 calls to illegalArgumentmethods could have been realized as aspects.Instance of a thing. The transaction with the log message “Re-place every single instance of something instanceof MThingwith ModelFacade.isAThing(something)” inserted manyisA calls to the source code. isA methods look as follows:public boolean isAClassifier(Object handle) {return handle instanceof MClassifier;}There exist 111 methods of the above form; these methodscould have easily been generated with aspects.In our previous work [1] we showed that mining cross-cutting con-cerns from version archives has a high precision, for the top 20aspect candidates of ECLIPSE we reached up to 90%. Measuringrecall requires knowing all aspect candidates, which is typicallyonly possible for a few small benchmark projects.5. CONCLUSIONCo-addition of method calls identifies usage patterns; a usage pat-tern may be actually a cross-cutting concern when all locationswhere calls were added call the same set of locations. Both usagepattern and cross-cutting concerns can be identified by mining ver-sion archives, as demonstrated by the ones we found in ArgoUML.Usage patterns and cross-cutting concerns have several benefits.Mining usage patterns can locate defects in software and supportsprogram understanding. Knowing cross-cuttings concerns helps toreduce maintenance effort and is the prerequisite for refactoring alegacy system into an aspect-oriented design.For a more detailed description of our mining approaches, we re-fer to our publications on finding usage patterns [3] and identifyingcross-cutting concerns [1].6. REFERENCES[1] S. Breu and T. Zimmermann. Mining Aspects from History.Submitted for publication.[2] eROSE. Guiding Programmers in Eclipse.http://www.st.cs.uni-sb.de/softevo/erose/.[3] V. B. Livshits and T. Zimmermann. Dynamine: FindingCommon Error Patterns by Mining Software RevisionHistories. In Proc. Europ. Software Engineering Conf./ACMSIGSOFT Symp. on the Foundations of Software Engineering,2005.[4] MSR. Mining Challenge 2006.http://msr.uwaterloo.ca/challenge/.[5] Prudsys AG. XELOPES Library.http://www.prudsys.com/Produkte/Algorithmen/Xelopes/.

Mining Additions of Method Calls in ArgoUML

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Mining additions of method calls in ArgoUML

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Mining Additions of Method Calls in ArgoUML

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