Comparing Reuse Mechanisms for Model Transformation Languages: Design for an Empirical Study

Abstract

Reuse mechanisms for model transformation languages help avoid duplication, thereby increasing maintainability and enabling a more systematic overall development process. As the introduction of such reuse mechanisms to model transformation languages is still in its preliminary stages, however, language designers are currently faced with the challenge of choosing from amongst diverse proposed approaches. Although there are a few surveys comparing syntactic and semantic di erences, there is still a need for empirical studies on the actual e ectiveness of di erent reuse mechanisms for transformation developers. In this paper, therefore, we present a study design for a controlled experiment to investigate the bene ts and drawbacks of two speci c reuse mechanisms for model transformation languages: rule re nement and variability-based rules. Both mechanisms are tailored to graph-based model transformation languages, yet represent two contrasting reuse paradigms: modularizing rules by composing them from smaller, shared fragments, versus maintaining a single, integrated representation via variability annotations. We propose to compare these two approaches using comprehension and bug- nding tasks to investigate understandability, and bug- xing and modi cation tasks to study changeability.

1. INTRODUCTION

Model transformations play a fundamental role in ModelDriven Engineering (MDE), where software systems are produced by the continuous re nement, translation, and synchronization of models [ 19 ].

As the eld matures and MDE approaches are faced with increasingly complex development tasks, the size and complexity of required model transformations also increases. Developing and especially maintaining such large transformations is challenging without dedicated support for reusing parts of existing transformations [ 22 ]. As such reuse mechanisms for model transformation languages are just starting to emerge [ 14 ], however, transformation developers are often forced to resort to code or pattern duplication, well-known for its manifold maintainability-related drawbacks.

But how can language designers decide which reuse mechanisms to integrate in their model transformation languages? While some surveys exist comparing syntactic and semantic di erences of various proposed reuse mechanisms This research was partially supported by the research project Visual Privacy Management in User Centric Open Environments (supported by the EU's Horizon 2020 programme, Proposal number: 653642). (cf., e.g., [ 14 ]), there exist no empirical studies of various transformation reuse mechanisms and their actual usefulness for transformation developers.

In this work, therefore, we present our design for a controlled experiment that aims to explore the bene ts and drawbacks of the proposed reuse mechanisms for model transformation languages. Speci cally, we intend to compare two recently emerging reuse mechanisms: rule re nement [ 4 ] and variability-based rules [ 24 ]. This choice is motivated by the fact that both mechanisms address the same technical scope - the reuse of graph-based model transformations [ 7 ] - while contrasting in the employed reuse paradigm: rule re nement reuses transformation rules by breaking them into smaller fragments that can be assembled using a composition mechanism, while a variability-based rule represents a family of multiple, similar rules in an integrated form, using variability annotations to denote individual parts. This distinction between composition- and annotation-based approaches is well-known from the domain of software product lines [ 11 ].

We are interested in the in uence of these reuse mechanisms on maintainability, a main quality aspect of software artifacts [ 1 ] that is particularly important in the face of continuous development and evolution. More speci cally, our study targets the maintainability aspects of understandability and changeability of model transformations [ 18, 26 ]. Given a group of participants, understandability can be quanti ed in terms of correctness, completion time, and perceived e ort during bug- nding and multiple-choice style comprehension tasks. To measure changeability, we plan to use bug- xing and modi cation tasks, again using correctness, completion time, and perceived e ort for quanti cation.

The rest of the paper is structured as follows: Section 2 discusses related work. Section 3 introduces a motivating running example that is used to explain the two reuse mechanisms for the comparison. Section 4 presents our study design with an outline of the experimental variables of the set-up, while Section 5 concludes. 2.

RELATED WORK

The planned study is the rst empirical investigation of transformation reuse mechanisms. Empirical studies in the area of model transformation have so far been con ned to the evaluation of transformation languages and approaches. Acretoaie et. al [ 2 ] have compared their usability-oriented Visual Model Transformation Language to traditional visual and textual languages. They found that their approach establishes a compromise between e ort and correctness. In an evaluation of their transformation-by-example approach, Avazpour et al. [ 6 ] found that developers perceive the provided interaction-based tool support as useful during the development of transformations. Guana and Stroulia [ 9 ] propose a study design to understand developer performance during the development of model-to-code transformations and measure the e ect of traceability visualizations. Conversely, only reuse mechanisms for code have been investigated so far, mostly from a product line perspective [ 8, 15 ]. applications where possibly hundreds of rules are required, such pattern duplication can quickly lead to a maintenance nightmare as changes, e.g., bug xes, have to be consistently applied to multiple, independent rules without tool support. 3.1

Rule refinement

The basic idea of rule re nement [ 4 ] is to enable a exible sharing and composition of common rule fragments. Pattern duplication can be avoided in this way by including the 3. MOTIVATING SCENARIO same fragment as a part of multiple rules. Rule re nement Our motivating scenario (adapted from [ 23 ]) is a simpli- can be applied to our running example to result in a network ed move method refactoring, implemented via three trans- of rule fragments as depicted in Fig. 2. Rule A remains unformation rules depicted in Fig. 1: The rst rule A transfers changed and is regarded as \resolved" as it does not re ne ethod(src,trg,ma) method m from its current containing class s to another any other rule fragment (it has no outgoing \inheritance" class t. This rule is a graph mtroavnesMfoerthmodaAtidodnWrrauplepetrh(sarct,tprgr,ems)er- arrows). Rule B is reduced to a re nement of A, i.e., it is preserve» «prevseersvea»ll e«lepmreseenrtvse»marked with pres«eprrveese,rdvee»let«epsreaslelrveel»eme«nptrseserve»produced by merging rule A with the speci ed re nement. fields :Fieldmarked wittyhpedelete, and creates allfieelldemsent:Fsiemldarked withtype This merge operator is relatively straightforward: The disrve» create. Su«cphrersuerlvees»are on:«lCyplareassspeprvliec»able if all preserved and :«Cplaresssejrovien»t union of both patterns (in general of multiple patterns) deleted ele m:Celanssts can be matncahmede=sinrc a hos«tdmeleotde»el. namies=tfrogrmed, and then all elements with the same identi er are e=src Rules B anndamCe=rtergpresent var«iacnretasteo»f A:mBethcordesates a wra- glued together. Similarly, rule C is speci ed as a re nement deetlheoted»s «prespeprveer»meth«mocedrtehawotedf»sor simple delegmaettihoondsto the «mpreetsheerocvtdes»mofththaet«mcertehaoted»ssoof lvBi,nrgesruullteinBgainnd tthheensarmepeeaptaitntgertnheassaimneFmiger1gibnyg prrsotcerses:Methisodto be moved. This can b:«Mecreeutahsteoedd» to limit the e

:Method namreef=amctoring and avoid having to change all exniasmtine=gmclients

name=m of the class s. Rule C additionally creates an annotation that marks w as deprecated to discourage future usage of mthoevewMreathpopdeArddmDeetphreocda,tewdWhircahppcear(nsrcb,terge,vme)ntually deleted.

with the speci ed re nement for rule C. Note that the merge operator requires the manual speci cation of identical elements, which is done by assigning names (e.g., s, me) to pattern variables, a concept that is not to be confused with parameters (e.g., src, trg, m), which are used to propagate values across name attributes in this example. «preserve» :Class name=trg «create» methods «create» :Method name=m

«preserve» «preserve» :«2Cplnareasssme:«AeCrp:v=larmeses»rosscevrev(esr»cf,ietrldg«,spmrf)eies:«leFdprisvereeld»se:«rFvpieerel»dserve» type «preserve» «crneaamtee»=src «delete» methods «create» methods annotation «delete» «create» method«spreserve» :Annotation :Method value="Dep" name=:«mMpreetsheordve»

name=m «pr:eCslearsvse» «preserve» typneame=trg :Class «crneaamtee»=trg methods «create» methods

B: moveAndCreateDelegate(src, trg, m) «preserve» 5 «preserve» :Class :Class name=trg moveMentahmode=(ssrrcc,trg,m) «preserve» «preserve»

fields :Field «create» «preserve» methods :Class ««pcrreesaetrev»e» «preserve» mfeitehldodss :Field name«=csrrecate»

:Method «mcertehanotaedm»se=m «preserve» «preserve»

type :Class deprecate -> wrappenrame=trg

This simple example illustrates how multiple transformation rules can \share" a common part that must be repeated in each variant if no reuse mechanism is provided. In realistic

In stark contrast to rule re nement, variability-based rules [ 21, 23, 24 ] enable an integrated representation of multiple rules in a single diagram. The variability-based rule for our running example is depicted in Fig. 3. The rule comprises two con guration options that are denoted via variability annotations, e.g., the annotation [wrapper] is used to indicate which edges and nodes in the diagram are only present when con guration option wrapper is set to true. Note that dependencies between con guration options can be speci ed, e.g., the dependency deprecate -> wrapper (upper-left corner) is used to indicate that the con guration option deprecate requires wrapper, i.e., all elements annotated with wrapper are also present in the deprecate variant of the rule.

moveMethod(src,trg,m)

deprecate -> wrapper «preserve» «preserve» «preserve» «preserve» «preserve» :Class fields :Field type :Class name=src name=trg the in uence of confounding factors (e.g., participant expertise) is reduced as each participant serves as their own control. A main threat to this kind of study design are learning e ects: over the course of the experiments, participants become more experienced and might carry over gained experience among tasks. A viable mitigation to this threat is to vary the order of treatments among participants. We discuss additional threats and mitigation strategies in Sec. 4.3.

To discuss the di erent dimensions that in uence our experiment, we rst outline its variables in the following. We then specify our research plan as one possible path through the design space spanned by these variables. Finally, we describe potential threats to validity in our set-up, and our means of mitigating these threats. 4.1

Experimental variables

4.1.1 Independent variables

As the single independent variable in the planned experioveMethod(src,trgT,mh)is scenario illustrates a set of maadidnWtarainppaebri(lsirtcy,mt)rade-odesprecate(mme)nextt,ewndes measure the e ects of the employed reuse mecha«preservteh»at we aim to«perxepselorvree»in our studeyx.tends

moveMethocdo(nsrsci,dtregr,med) maed-dWrappneirs(smrc.,mF)or RQ1 and RQ3, only the data points of the two fieldschan:«Fpiisermeldsesrhvee»lp avoitdyppeattern duplicationB, owthhich may simplify proposed reuse mechanisms are considered, whereas RQ2 inpreserve» the consistent editin«gproefsemrveu»ltiple va«rpiraensetrsvea»t once. In ad«-create»corporates the data points for the no-reuse case as well. Class dition, if applied skil:lCfluasllsy, rule re ne:Cmlaessnt can lead to com:-Method name=src prehensible and maintnaaimnea=btlreg modulanramrue=lesrcf«rcargematee»nts, re- name=m4.1.2 Dependent variables «delete» ect«ipnrgesaenrvein»depe«ncdreeantte»handling and separatiomnetohfocdosncerns a«ncnTroehtaaetteivo»anriables we want to observe as a result of the expemethodisn th:Me ettrhaondsformatmioenth.odVsariability-ba«scerdeartue»les improve nota«-create»riment are the following:

name=m tional compactness drastically, there:bMyetphootdentially reducin:gAnnotationCorrectness score. We plan to measure correctness bareading e ort: it is well-known that hnuammea=nms struggle with value="sDeedp"on how well participants are able to solve certain tasks. processing more than 7 2 units of information at once [ 17 ]. As these tasks might not be directly comparable, counting On the downside, as they require developers to derive the the number of correctly solved tasks only provides ordinal included rule variants in their minds, both approaches may values. increase cognitive overhead. In particular, variability-based Perceived e ort. Participants will be asked to rate the rules may su er from their increased amount of informa- perceived e ort it took to solve all tasks using a suitable tion in each individual diagram, comparable to the case of ordinal scale ranging from, e.g., 0 to 10 to quantify e ort. variability-annotated code, where a decrease in developer Completion time. For all tasks, completion times will performance was found [ 15 ]. be measured for all participants. This qualitative value can be contrasted with correctness scores and perceived e ort to 4. STUDY DESIGN yield further insights.

User preference. Finally, regardless of correctness, pertheTofoilnlovwesitniggarteesetahrechideqnuteisteiodntsr:ade-o s further, we propose rceeuivseedmeecohrat,niasnmd tchoemypplerteifoenr atinmdee,nwjoeysehdalulsainskg uthseermswoshti.ch RQ1: Which reuse mechanism is most bene cial for For statistical analysis, we intend to apply the Wilcoxon developers in terms of understandability and changea- signed-rank test [ 28 ], which is a robust non-parametric test bility? to identify signi cant di erences between two treatments. It assumes data measured at least on an ordinal scale, which RQ2: How do the studied reuse mechanisms compare applies to all of our dependent variables. to o ering no reuse at all? A remaining issue concerns the fact that we are interested RQ3: Can the understandability and changeability of in two di erent quality goals that should ideally be studied the selected reuse mechanisms be further improved via as separate entities. Yet, as changeability clearly depends the available tool support? on understandability, it is not obvious how these two di erent goals can be studied in isolation. To this end, a viable method is to collect performance data for both goals, and to subtract the understandability from the changeability results. For instance, the time required to actually change the a ected rules can be obtained from subtracting the time required to understand the rules from the full completion time. A prerequisite for this method is that subtraction is supported by the underlying scale, which is the case for completion times.

To address these research questions, we plan to administer a set of comprehension and implementation tasks to participants who are familiar with graph-based model transformations. Such participants will be recruited from research groups, as well as master-level model-driven engineering courses.

We plan to apply a crossover trial, a variant of the withinsubject design [ 10 ]: in this design, all subjects are sequentially exposed to each treatment. This choice is motivated by the statistical e ciency of the design: it allows the number of required participants to be minimized. Moreover,

The design space of our controlled variables comprises the 4.1.3

Controlled variables

Independent variable Controlled variables

Dependent variables

Reuse mechanism Transformation language Example type Task type

Environment Correctness score Perceived e ort Completion time User preference

Possible values f Rule re nement, variability-based rules, no reuse g f Henshin, ATL, EOL, ... g f queries, endogenous, exogenous g f bug- nding, quiz, bug- xing, modi cation task g f pen-and-paper, tool g Ordinal Ordinal Interval

Ordinal following dimensions:

Transformation language. The variety of available model transformation languages is usually classi ed along a number of main paradigms, most prominently imperative, hybrid, and graph-based model transformations [ 7, 16 ]. In the planned study, we aim to investigate reuse in the context of Henshin [ 5 ], a graph-based language. The reason for this choice is twofold: rst, examples and tool support for one of the supported reused mechanisms are readily available in Henshin (convenience sampling). Second, the graphical syntax of Henshin closely resembles that of a group of languages including eMo on [ 3 ], EMorF [ 12 ], and QVTs [ 29 ], rendering it a promising choice to obtain results which might also generalize to these languages. In addition, Henshin is under active development and in widespread use.1 Our study will be based on bug- nding and bug- xing tasks, since these tasks can avoid lengthy textual task descriptions that might be hard to match to the presented visual representations [ 27 ]. Our example rules will mostly be endogenous, such as those shown in the running example, allowing us to establish a compromise between external and internal validity.

Example types. In the choice of example transformations, we need to account for the inherent trade-o between external and internal validity [ 20 ]: by using simple and easyto-understand examples, we can mitigate the e ect of participant heterogeneity, but risk ending up with an experiment that is not realistic enough. The simplest transformation that can be expressed using rules is a query { a rule performing no modi cations of the input model. Queries are generally easy to understand; yet in realistic settings they are likely to be expressed using dedicated query languages. Endogenous transformations { transformations that update the input model or translate it to a model of the same language { are a typical use-case for graph-based model transformation, but require an understanding of its underlying replacement mechanism. Exogenous transformations, creating an output model of a di erent language, are most complicated as they require the understanding of two meta-models and their relations; yet they are particularly important for the typical forward-engineering process in model-driven engineering. Two other sub-dimensions of this dimension are the number of examples and the size of the individual examples.

We intend to conduct a pilot study to nd a suitable collection of examples that avoids participant fatigue, using a di erent set of participants than in the actual study to avoid 1For a selection of publications describing Henshin's use in numerous research projects, see https://www.eclipse.org/ henshin/publications.php history e ects. Other goals of the pilot study are to determine if we can extract any meaningful results (e.g. maybe all task are too di cult and we do not gather su cient useful data), and to see if our assumptions about participant's abilities hold true.

Task types. To study understandability and changeability, we plan to administer tasks allowing us to quantify these properties. The understandability of a software artifact can be investigated using bug- nding tasks or quiz-style comprehension questions. In the example, a bug- nding task would be to swap the delete and create edges and have the user nd and mark this error on a printout. A comprehension question could be \How many individual rules can be derived from this representation?". Changeability refers to the ease of modifying or extending the functionality of a system. Two possible kinds of tasks to study changeability are the xing of a preconceived bug, such as the one described above, and the implementation of a feature request such as \I need a set of transformation rules with the same behavior as the given ones, but with one modi cation: the target class must have a eld whose type is the source class". A challenge for the study design lies in choosing a feasible subset of all tasks so as to avoid exhausting the participants. To this end, we plan to use bug- nding and bug- xing tasks, since these tasks can avoid lengthy textual task descriptions that might be hard to match against the presented visual representations [ 27 ].

Environment. Another balancing act between control and remaining adequately realistic concerns the experimental environment. By setting up the participants in a controlled pencil-and-paper environment, we can eliminate confounding factors related to the tool implementations, for instance, di erences in visual appearance. Such a controlled environment is, however, not really comparable to the realworld environment that actual users of the reuse mechanisms nd themselves in, especially in the case of changeability tasks. Moreover, RQ3 can only be studied by comparing the reuse mechanisms with and without the actual tool support provided. As of now, tool support for rule re nement [ 13 ] and VB rules [ 25 ] exists, but is implemented within di erent model transformation languages and corresponding tool environments { eMo on and Henshin. To ensure optimal control during the experimental comparison, both mechanisms should be implemented for the same language and tool environment. 4.2

Quantitative data

To gain additional insight about the perceived bene ts and pitfalls of both reuse approaches, we also plan to collect quantitative data, using text elds in the task sheet to collect participants' opinions after their completion of the experiment. 4.3

Potential threats to validity

In addition to the threats already described in the previous discussion, we plan to mitigate researcher bias by striving to ensure that participants are unable to directly map reuse mechanisms to researchers, e.g., by anonymising tool names.

To avoid selection bias in favor of volunteers, whose level of motivation does not re ect that of the whole population, we will o er a small prize among participants.

Finally, the participants' performance can be confounded by relevant prior knowledge, e.g., from exercise in the area of software product lines and other annotation-based model families. To study the e ect of prior knowledge, we plan to ask participants to rate their own prior knowledge of relevant topics.

CONCLUSION

In this paper, we present our study design for an empirical comparison of two reuse mechanisms for graph-based model transformation languages. The selected reuse mechanisms exemplify the contrasting reuse paradigms of modularizing a set of rules and merging similar rules in a variabilityannotated representation.

Our prior expectations are as follows: we expect re nements to be (perhaps only slightly) more understandable but less changeable than variability rules (RQ 1), both reuse mechanisms to substantially improve changeability but (at least without substantial training) reduce understandability (RQ 2), and that appropriate tool support plays a crucial role in both cases (RQ 3). It remains to be seen if our experiment results will support or refute these initial hypotheses. Addressing the trade-o between di erent forms of experimental validity [ 20 ], our set-up aims to establish a compromise between internal and external validity. A series of follow-up studies moving the spotlight in either direction can lead to a more comprehensive picture.

The results of our study will provide evidence to designers of model transformation languages who are planning to include a reuse mechanism within their language. In a broader context, we hope to shed a new light on the discussion of when and how software reuse can be established in a manner that is most bene cial to all involved stakeholders.

Acknowledgements: The authors wish to thank Vlad Acret,oaie and the anonymous reviewers for their careful reading of this paper and their detailed comments and suggestions.

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