On the Reusable Specification of Non-functional Properties in DSLs

On he Reusable Speci ica ion o Non- unc ional P ope ies in DSLs F ancisco Du ´an1, S e en Zschale 2, and Ja ie T oya1 1Depa amen o de Lenguajes y Ciencias de la Compu aci´on Uni e sidad de M´alaga {du an,ja ie c}@lcc.uma.es 2Depa men o In o ma ics King’s College London [email p o ec ed] Abs ac . Domain-speci ic languages (DSLs) a e an impo an ool o e ec i e sys em de elopmen . They p o ide concep s ha a e close o he p oblem do- main and allow analysis as well as gene a ion o ull solu ion implemen a ions. Howe e , his comes a he cos o ha ing o de elop a new language o e e y new domain. To make hei de elopmen e icien , we mus be able o cons uc DSLs as much as possible om eusable building blocks. In his pape , we dis- cuss how such building blocks can be cons uc ed o he speci ica ion and anal- ysis o a ange o non- unc ional p ope ies, such as, o example, h oughpu , esponse ime, o eliabili y p ope ies. We assume DSL seman ics o be p o- ided h ough a se o ans o ma ion ules, which enables a ange o analyses based on model checking. We demons a e new concep s o de ining language modules o he speci ica ion o non- unc ional p ope ies, show how hese can be in eg a ed wi h base DSL speci ica ions, and p o ide a numbe o syn ac ic condi ions ha we p o e main ain he seman ics o he base DSL e en in he p esence o non- unc ional–p ope y speci ica ions. 1 In oduc ion Domain-speci ic languages (DSLs) a e an impo an ool o eaping he p oposed ben- e i s o model-d i en enginee ing [1]. DSLs a e languages based on concep s close o he p oblem domain han he echnical solu ion. They a e, he e o e, a good way o al- low domain-expe s, who may lack p og amming skills, o cons uc o pa icipa e in cons uc ing subs an ial pa s o new sys ems. In addi ion, because much mo e knowl- edge o he domain is a ailable when in e p e ing s a emen s in a DSL, i is possible o p o ide much mo e ex ensi e code gene a ion; his can enable comple e gene a ion o unning sys ems om a ela i ely simple DSL-based model [2]. Howe e , o DSLs o be e ec i e, hey may need o be implemen ed o e y na ow domains [1], which implies ha a la ge numbe o DSLs needs o be implemen ed. This equi es highly e icien echniques o de eloping new DSLs, ideally based on an abili y o euse and compose pa ial languages o new domains. In he design o so wa e sys ems, many esea che s dis inguish be ween unc ional and non- unc ional p ope ies (NFPs)—also some imes e e ed o as ex a- unc ional p ope ies o quali y o se ice. While unc ional p ope ies a e cons ain s on wha he so wa e sys em does, NFPs a e cons ain s on how i does i — o example, how much esou ces a e used o how long i akes o p ocess an indi idual eques . NFPs a e impo an o he o e all quali y o a sys em, so hey clea ly need o be aken in o accoun h oughou de elopmen . We need o be able o p edic and analyse NFPs om an ea ly s age o de elopmen , so as o a oid cos ly e-design o e-implemen a ion a a la e s age. When de eloping sys ems based on DSLs, hese DSLs, consequen ly, need o include an abili y o exp ess and analyse ele an NFPs. Howe e , he analysis o NFPs is di icul and usually equi es subs an ial specialis expe ise. In eg a ing an abili y o speci y NFPs in o DSLs can subs an ially inc ease he e o equi ed o build a DSL. In his pape , we p opose a echnique o allowing NFP speci ica ion o be encapsula ed in o eusable DSL componen s. This way, he bu den o speci ying he NFPs o DSLs is d as ically educed, and specialis expe ise is mainly equi ed when he language componen is cons uc ed. De eloping new DSLs capable o speci ying pa icula NFPs in he con ex o a pa icula domain hen becomes a ma e o wea ing in he NFP’s language componen . The e-Mo ions language and sys em allows he de ini ion o isual DSLs and hei se- man ics h ough in-place model- ans o ma ion ules, p o iding suppo o hei anal- ysis h ough simula ion o model checking in Maude [3]. In [4], T oya, Ri e a, and Vallecillo build on he ideas o he e-Mo ions amewo k [5,6] o keep ack o speci ic NFPs by adding auxilia y objec s o DSLs. Howe e , hei app oach s ill equi es he NFP speci ica ion and analysis componen o be ede ined om sc a ch o e e y new DSL. In his pape we build on hei wo k, bu aim o modula ise he NFP pa in o i s own language componen . To do so, we ake inspi a ion om he wo k in [7] whe e Zschale in oduced he no ion o con ex models o p o ide an in e ace be ween TLA+ speci ica ions o non- unc ional and unc ional p ope ies. We will use pa ame isa ion o e me a-models o achie e a simila e ec o ou language componen s. Speci ically, we p esen a o mal amewo k o such language componen s, syn ac ic condi ions o hei consis ency and p oo s o hese condi ions. We also p esen a basic p o o ype im- plemen ing hese ideas in he con ex o e-Mo ions. Howe e , a ull in eg a ion is no in he scope o his cu en pape . While ou p o o ype and o iginal mo i a ion a e o he case o e-Mo ions, bo h ou app oach and o mal amewo k a e mo e gene al. They can be applied o any DSL speci ica ion whose seman ics a e based on model ans o ma ions. Mo eo e , while ou wo k is clea ly mo i a ed om he need o modula ising NFP speci ica ions, he o mal amewo k co e s a bi a y conse a i e ex ensions o such DSLs, gua an eeing hem o be spec a i e in he sense o [8]. The emainde o his pape is s uc u ed as ollows: In Sec ion 2, we discuss a de- ailed mo i a ing example o explain he ision o wha we would like o achie e. Sec- ion 3 hen p esen s a o malisa ion o hese ideas oge he wi h consis ency condi ions and ske ches o hei p oo s (see [9] o addi ional de ails on his). Sec ion 4 b ie ly discusses ou ini ial p o o ype. Finally, Sec ion 5 discusses ela ed wo k ollowed by conclusions and an ou look o u u e wo k in Sec ion 6. Fig.1. P oduc ion line (a) me amodel and (b) conc e e syn ax ( om [4]) 2 Mo i a ing Example In his sec ion, we p esen an example o wha we wan o achie e. This is based on wo k p esen ed by T oya, Ri e a, and Vallecillo in [4]. Thei wo k de ines DSLs om wo pa s: a me a-model o he language concep s and a se o ans o ma ion ules o speci y he beha iou al seman ics o he DSL. Figu e 1(a) shows he me amodel o a DSL o speci ying p oduc ion-line sys ems, o p oducing hamme s ou o hamme heads and handles, which a e gene a ed in e- spec i e machines, and anspo ed along he p oduc ion line ia con eyo s and ays. As usual in MDE-based DSLs, his me amodel de ines all he concep s o he language and hei in e connec ions; in sho , i p o ides he language’s abs ac syn ax. In addi- ion, a conc e e syn ax is p o ided. In he case o ou example, his is su icien ly well de ined by p o iding icons o each concep (see Figu e 1(b)); connec ions be ween concep s a e indica ed h ough a ows connec ing he co esponding icons. Ins ances o his DSL a e in ended as oken models [10]. Tha is, hey desc ibe a speci ic si ua ion and no he se o all possible si ua ions (as is he case, e.g., o class diag ams). The beha iou al seman ics o he DSL can, he e o e, be gi en by speci y- ing how models can e ol e; ha is, wha changes can occu in a pa icula si ua ion. This is speci ied h ough a se o model ans o ma ion ules. Figu e 2 shows an ex- ample o such a ule. The ule consis s o a le -hand side ma ching a si ua ion be o e he execu ion o he ule and a igh -hand side showing he esul o applying he ule.1 Speci ically, his ule shows how a new hamme is assembled: a hamme gene a o a 1The e a e some o he pa s o he ule, bu hey a e no ele an o ou cu en discussion. Fo a mo e de ailed discussion, please e e o ma e ial on e-Mo ions [5,6]. Fig.2. Assemble ule indica ing how a new hamme is assembled ( om [4]) has an incoming ay o pa s and is connec ed o an ou going con eyo bel . Whene e he e is a handle and a head a ailable, and he e is space in he con eyo o a leas one pa (speci ied by an OCL cons ain in he le -hand side o he ule), he hamme gene a o can assemble hem in o a hamme . The new hamme is added o he pa s se o he ou going con eyo bel . The comple e seman ics o ou p oduc ion-line DSL is cons uc ed om a numbe o such ules co e ing all kinds o a omic s eps ha can occu .2 Fo p oduc ion line sys ems, we a e in e es ed in a numbe o non- unc ional p op- e ies. Fo example, we would like o assess he h oughpu o he p oduc line o how long i akes o a hamme o be p oduced.3We can achie e his by ex ending ou DSL speci ica ion wi h obse e s [4]. Di e en om [4], he e we sugges de ining speci i- ca ion languages o obse e s en i ely sepa a ely om any speci ic DSL. We will use he same mechanisms we used o de ining he p oduc line DSL o de ine a DSL ha enables us o speci y h oughpu o p oduc ion ime o sys ems. Figu e 3(a) shows he me a-model o a DSL o speci ying p oduc ion ime. Two hings should be no ed abou his me a-model: 2The comple e speci ica ion o he P oduc ion Line example can be ound a h p://a enea.lcc.uma.es/E-mo ions/PLSExample. 3We use his p ope y as an example he e. O he p ope ies can be de ined easily in a simila ein as shown in [4] and on h p://a enea.lcc.uma.es/index.php/ Main Page/Resou ces/E-mo ions/PLSObExample. MMRespTime p Se e , Queue, Reques (a) Me a-model. (b) Conc e e syn ax. Fig.3. Me a-model and conc e e syn ax o esponse ime obse e 1. I de ines no concep p oduc ion ime. Ins ead, i de ines some hing called esponse ime, which is a mo e gene ic concep . P oduc ion ime is eally only meaning ul in he con ex o p oduc ion sys ems. Howe e , he gene al concep o esponse ime co e s his su icien ly well. 2. I is a pa ame ic model (i.e., a model empla e). The concep s o Se e ,Queue, and Reques and hei in e connec ions a e pa ame e s o he me a-model, and hey a e shaded in g ey o illus a ion pu poses. We use hem o desc ibe in which si ua ions esponse ime can be speci ied, bu hese concep swill need o be mapped o conc e e concep s in a speci ic DSL. Figu e 3(b) shows he conc e e syn ax o he esponse ime obse e objec . Whene e ha obse e appea s in a beha iou al ule, i will be ep esen ed by ha g aphical symbol. Figu e 4 shows an example ans o ma ion ule de ining he seman ics o he e- sponse ime obse e . This s a es ha i he e is a se e wi h an in queue and an ou queue and he e ini ially a e some eques s (a leas one) in he in queue, and he ou queue con ains some eques s a e ule execu ion, he las esponse ime should be eco ded o ha e been equal o he ime i ook he ule o execu e. Simila ules need o be w i en o cap u e o he si ua ions in which esponse ime needs o be measu ed, o example, whe e a eques s ays a a se e o some ime, o whe e a se e does no ha e an explici in o ou queue. No e ha he ule in Figu e 4 looks di e en om he ule shown in Figu e 2. This is because he ule is ac ually a ule ans o ma ion, while Figu e 2 is a ans o ma ion ule. The uppe pa o Figu e 4 (shaded in g ey o illus a ion pu poses) is a pa e n o que y desc ibing ans o ma ion ules ha need o be ex ended o include esponse- ime accoun ing. The lowe pa desc ibes he ex ensions ha a e equi ed. So, in addi ion o eading Figu e 4 as a ‘no mal’ ans o ma ion ule (as we ha e done in he p e ious pa ag aph),we can also ead i as a ule ans o ma ions a ing: “Find all ules ha ma ch he shaded pa e n and add ResponseTime objec s o hei le and igh -hand sides as desc ibed.” In e ec , obse e models become highe -o de ans o ma ions [11]. As he ules in obse e models a e ule ans o ma ions, we can allow some addi- ional concep s o be exp essed. Fo example, Figu e 4 uses mul iplici ies o exp ess ha Fig.4. Sample esponse ime ule he e may be an a bi a y numbe o eques s (bu a leas one) associa ed wi h a queue. This is no allowed in ‘no mal’ ans o ma ion ules ( he e we need o explici ly show each ins ance). Howe e , using mul iplici ies allows exp essing pa e ns o be ma ched agains ans o ma ion ules—a ma ch is gi en by any ule ha has he indica ed numbe o ins ances in i s le - o igh -hand side. To use ou esponse- ime language o allow speci ica ion o p oduc ion ime o ham- me s in ou p oduc ion-line DSL, we need o wea e he wo languages oge he . Fo his, we need o p o idea binding om he pa ame e so he esponse- imeme a-model (Figu e 3(a)) o concep s in he p oduc ion-line me a-model (Figu e 1(a)). Speci ically, we bind: –Se e o Assemble as we a e in e es ed in measu ing esponse ime o his pa icula machine; –Queue o Limi edCon aine as he Assemble machine is o be connec ed o an a bi a y Limi edCon aine o queuing incoming and ou going pa s; –Reques o Pa as Assemble only does some hing when he e a e Pa s o be p ocessed; and – Associa ions: •The in and ou associa ions om Se e o Queue a e bound o he co e- sponding in and ou associa ions om Machine o T ay and Con eyo , espec i ely; and •The associa ion om Queue o Reques is bound o he associa ion om Con aine o Pa . Fig.5. Wo en me a-model o measu ing p oduc ion ime o he hamme assemble (highligh ing added o illus a ion pu poses) Wea ing he me a-models acco ding o his binding p oduces he me a-model in Fig- u e 5. The wea ing p ocess has added he ResponseTime concep o he me a-model. No ice ha he wea ing p ocess also ensu es ha only sensible wo en me a-models can be p oduced: o a gi en binding o pa ame e s, he e needs o be a ma ch be ween he cons ain s exp essed in he obse e me a-model and he DSL me a-model. We will discuss his issue in mo e o mal de ail in Sec ion 3. The binding also enables us o execu e he ule ans o ma ions speci ied in he ob- se e language. Fo example, he ule in Figu e 2 ma ches he pa e n in Figu e 4, gi en his binding: In he le -hand side, he e is a Se e (Assemble) wi h an in-Queue (T ay) ha holds wo Reques s(Handle and Head) and an ou -Queue (Con ey- o ). In he igh -hand side, he e is a Se e (Assemble) wi h an in-Queue (T ay) and an ou -Queue (Con eyo ) ha holds one Reques (Hamme ). Consequen ly, we can apply he ule ans o ma ion om Figu e 4, which p oduces he ule shown in Figu e 6. This ule is equi alen o wha would ha e been w i en manually. Clea ly, such a sepa a ion o conce ns be ween a speci ica ion o he base DSL and speci ica ions o languages o non- unc ional p ope ies is desi able. In he nex sec- ion, we discuss he o mal amewo k equi ed o his and how we can dis inguish sa e bindings om unsa e ones. 3 Fo mal F amewo k G aph ans o ma ion [12] is a o mal, g aphical and na u al way o exp essing g aph manipula ion based on ules. In g aph-based modelling (and me a-modelling), g aphs a e used o de ine he s a ic s uc u es, such as class and objec ones, which ep esen Fig.6. Resul o wea ing Figu e 2 and Figu e 4 isual alphabe s and sen ences o e hem. We o malise ou app oach using he yped g aph ans o ma ion app oach, speci ically he Double Pushou (DPO) algeb aic ap- p oach, wi h posi i e and nega i e applica ion condi ions [13]. Ou g aphs a e, in pa - icula , yped a ibu ed g aphs [14]. We howe e ca y on ou o malisa ion o weak adhesi e high-le el eplacemen (HLR) ca ego ies (see [15]). The concep s o adhesi e and (weak) adhesi e HLR ca ego ies abs ac he oun- da ions o a gene al class o models, and comes oge he wi h a collec ion o gene al seman ic echniques. Thus, e.g., gi en p oo s o adhesi e HLR ca ego ies o gene al esul s such as he Local Chu ch-Rosse , o he Pa allelism and Concu ency Theo em, hey a e au oma ically alid o any ca ego y which is p o ed an adhesi e HLR ca - ego y. This amewo k has been a b eak- h ough o he DPO app oach o algeb aic g aph ans o ma ion, o which mos main esul s can be p o en in hese ca ego ical amewo ks, and ins an ia ed o any HLR sys em. One o hese cases is he one o in- e es o us: he ca ego y o yped a ibu ed g aphs was p o en o be an adhesi e HLR ca ego y in [14]. In his sec ion, we p esen a o mal amewo k o wha i means o de ine speci i- ca ion languages o non- unc ional p ope ies sepa a ely o ‘no mal’ DSLs, and in a way ha can be eused ac oss such DSLs. To his end, we will i s abs ac away om he conc e e ep esen a ion o languages and models in e-Mo ions [5,6] ha we ha e used in Sec ion 2. Ins ead, we will o mally ep esen he key elemen s o which such languages and models consis and he unc ions which a e used o manipula e hem. MObs MMObs  RlsObs MDSL MMDSL  RlsDSL Binding BMM  BRls MDSL (MMDSL Binding MMObs) (RlsDSL Binding RlsObs) Fig.7. A chi ec u e o he o mal amewo k Figu e 7 p o ides a g aphical o e iew o he o mal amewo k we a e p oposing. I can be seen ha his consis s o i e pa s: 1. MDSL: The speci ica ion o a DSL (wi hou any no ion o non- unc ional p ope - ies); 2. MObs : The speci ica ion o a language o modelling non- unc ional p ope ies o in e es ; 3. Binding: An a e ac exp essing how he pa ame e s o MObs should be ins an ia ed wi h concep s om MDSL in o de o wea e he wo languages; 4. ⊗: A unc ion ha pe o ms he ac ual wea ing; and 5. M DSL: A DSL ha combines he speci ica ion o some unc ionali y (as pe MDSL) and some non- unc ional p ope ies (as pe MObs ). 3.1 The Models In ol ed and Thei Rela ionships Following he algeb aic g aph ans o ma ion app oach, a DSL can be seen as a yped g aph g amma . A yped g aph ans o ma ion sys em GTS =(TG,P)consis s o a ype g aph TG and a se o yped g aph p oduc ions P.A yped g aph g amma GG =(GTS ,S)consis s o a yped g aph ans o ma ion sys em GTS and a yped s a g aph S. A language is hen de ined by he se o g aphs eachable om Susing he ans o ma ion ules P. De ini ion 1 (DSL). The speci ica ion MXo a DSL Xis gi en by a me amodel MMX, ep esen ing he s uc u al concep s o he language, and a se o ans o ma ion ules RlsX, de ining i s beha iou al seman ics.  A me amodel is jus a ype g aph, and a ans o ma ion ule associa ed o i is a g aph p oduc ion yped o e he ype g aph p o ided by such me amodel. The languages MDSL and M DSL a e DSL speci ica ions. MObs is, essen ially, also a no mal DSL speci ica ion. No ice ha we assume a single obse e model MObs o each non- unc ional p ope y. I we needed se e al o hese p ope ies, we could con- side MObs o be he combina ion o he speci ica ions o hese non- unc ional p ope - ies, o we could i e a e he p ocess by ins an ia ing M DSLonce ob ained wi h a second obse e s model MObsp oducing a esul ing speci ica ion M  DSL, which could again be ins an ia ed by ano he obse e s model MObs ,e c. means inse ing he ResponseTime class, adding i s a ibu es and es ablishing he espTime e e ence among Assemble and ResponseTime classes. As o he ou pu GCS ile, i means adding all he necessa y da a ega ding he conc e e syn ax o he ResponseTime class. Be ween i s inpu s, he second ans o ma ion, Wea eBeh.a l, akes he models p oduced by he i s ans o ma ion. I pe o ms in a simila way. The i s s ep is o copy all hose ules om RlsDSL in he ou pu model wi h he beha iou al ules. Nex , hose ules ha ing co espondences wi h ules in RlsObs a e deco a ed wi h obse e objec s, links and a ibu es. 5 Rela ed Wo k We discuss ela ed wo k in wo a eas: modelling o non- unc ional p ope ies and mod- ula language de ini ion. 5.1 Modelling o Non-Func ional P ope ies Modelling and analysis o non- unc ionalp ope ieshas been an ac i e esea ch a ea o a subs an ial amoun o ime al eady. Ou wo k is ela ed o o he wo k aiming o sup- po speci ica ion o a wide ange o non- unc ionalp ope ies— o example,languages such as QML [20], CQML [21], CQML+[22], o SLAng [23]. These languages ake a me a-modelling app oach o he speci ica ion o non- unc ional p ope ies in a wo-s ep p ocess: In a i s s ep, modelle s speci y non- unc ional cha ac e is ics— o example, pe o mance. These cha ac e is ics a e hen used in a second s ep o exp ess cons ain s o e applica ion models; ha is, non- unc ional p ope ies. This is simila o ou ap- p oach: An obse e model MObs e ec i ely de ines a non- unc ional cha ac e is ic. A wo en DSL M DSL can hen be used o model non- unc ional p ope ies. The ap- p oaches men ioned abo e di e in hei amoun o o mal igo (inc easing om QML o CQML+and SLAng) and he ype o sys ems hey suppo (all excep SLAng a e aimed a componen -based sys ems; SLAng is mean o se ice-based sys ems). They ypically do no p o ide ex ensi e suppo o analysis o he models c ea ed. Mo e o mal ende ings o hese concep s can be ound in [16] and [7]. The o me p esen s a o mal encoding o eal- ime p ope ies using so-called his o y-de e mined a iables, which a e hen used o model non- unc ional cha ac e is ics ha depend on ime. [7] ex ends his o a o mal amewo k o speci ying non- unc ional p ope ies o componen -based sys ems. While hese app oaches can po en ially enable p oo s o non- unc ional p ope ies, i is no clea how well hey a e sui ed o p edic i e analysis o sys em p ope ies— o example h ough simula ion. The app oach by T oya and Vallecillo [4] aims o add ess his issue by p o iding a speci ica ion based on obse e s and ans o ma ions. This enables p edic i e anal- ysis h ough simula ion based on an encoding in e-Mo ions [5, 6], which is ansla ed in o Maude. Howe e , hei app oach equi es he de ails o a non- unc ional cha ac e - is ic o be ede ined comple ely o each DSL. Ou p oposal is an ex ension o his wo k using ideas om [7, 16] o sepa a e he speci ica ion o non- unc ional cha ac e is ics om ha o he unc ional beha iou al seman ics o a DSL. 5.2 Modula Languages, Models, and T ans o ma ions We p opose o wea e wo language de ini ions: One language enables he (abs ac ) speci ica ion o a se o non- unc ional p ope ies while he second language ocuses en i ely on speci ying ele an beha iou s in a pa icula domain. Below we b ie ly e- iew some ela ed wo k in he gene al a ea o modula de ini ion o languages, models, and ans o ma ions. We discuss selec ed ela ed wo k in h ee a eas: 1. Modula de ini ion o languages; 2. Modula de ini ion o models; and 3. Modula de ini ion o model ans o ma ions. Modula De ini ion o Languages. The e is a la ge body o wo k on modula ly de in- ing compu e languages. Mos o his wo k (e.g., [24–26]) deals wi h ex ual languages and in pa icula wi h issues o composing con ex - ee g amma s. While he gene al idea o language composi ion is ele an o ou wo k, his speci ic s and o esea ch is pe haps less ela ed and will, he e o e, no be discussed in mo e de ail. Fo languages based on me a-modelling, he e is much less esea ch on language composi ion. Much o he wo k on model composi ion (see nex sub-sec ion) is o cou se o ele ance as me a-models a e models hemsel es. Ch is ian Wende’s wo k on ole-based language composi ion [27] is an app oach ha speci ically add esses he modula isa ion o me a-models. Fo a language module, Wende’s wo k allows he de - ini ion o a composi ion in e ace by allowing language designe s o use wo ypes o me a-model concep s: me a-classes and me a- oles. Me a-classes a e used as in no mal me a-modelling o exp ess he co e me a-model concep s. Me a- oles a e like me a- classes, howe e hey ac ually ep esen concep s o be p o ided by ano he language— including de ini ions o ope a ions and a ibu es, which a e le abs ac in he me a- ole. Me a- oles a e, hus, simila o ou use o me a-model pa ame e s in MMObs . Howe e , Wende’s wo k uses me a-class ope a ions o p o ide an ope a ional iew on languageseman ics, while we use model ans o ma ions o encode language seman ics. Modula Modelling. Ou no a ion o exp essing pa ame ised me a-models is based on how UML exp esses pa ame ised models. Simila no a ions ha e been used in aspec -o ien edmodelling (AOM) app oaches— o example,Theme/UML[28] o RAM [29]. Mo e gene ally, ou language composi ion echnique is based on he no ion o model wea ing om AOM. Theme/UML, RAM, o Reusewa e [30] a e examples o aspec -o ien ed modelling echniques, which a e asymme ic [31]; ha is, hey make a dis inc ion be ween a base model and an aspec model ( he model ha is pa ame ised) ha is wo en in o he base model. This is also ue o ou app oach: MDSL is he base model and MObs is he model ha is wo en in o i . The e is an al e na i e app oach o AOM ha is mo e symme ic and conside s all models o be wo en as equal. This is ypically based on iden i ying co esponding elemen s in di e en models and me ging hese. Examples a e UML package me ge o signa u e-based me ging [32]. Mos ypes o AOM also conside syn ac ic wea ing only, dis ega ding he seman ics o he mod- ula models. In con as , we explici ly conside he model seman ics and po ide o mal no ions ensu ing ha he composi ion does no es ic he se o beha iou s modelled in he base DSL. Modula Model T ans o ma ions. The seman ics o he languages we a e discussing a e exp essed using model ans o ma ions. As such, wo k on modula ising model ans o ma ions is o ele ance o ou wo k. Gene ally, his wo k can be dis inguished in o wo k on ex e nal and on in e nal modula isa ion o model ans o ma ions: The o me conside s a comple e model ans o ma ion as he uni o modula i y, while he la e aims o p o ide modula i y inside indi idual ans o ma ions [33]. As we a e modi ying he in e nals o he base ans o ma ion by adding in de ail desc ibed in he obse e ans o ma ion ules, ou app oach is an in e nal modula isa ion echnique. None heless, ideas om ex e nal composi ion app oaches a e o in e es o us. In pa - icula , he wo k on model yping and eusable model ans o ma ions p esen ed in [34] shows how he se o me a-model concep s e ec i ely used by a model ans o ma ion can be compu ed and how his can be used o make he ans o ma ions mo e eusable. This is simila o he way in which we use he pa ame ised pa o MMObs o make he obse e ans o ma ion ules mo e eusable and o adap hem o di e en DSLs. 6 Conclusions and Ou look We ha e p esen ed a o mal amewo k o language componen s o he speci ica ion o non- unc ional p ope ies (NFPs) in domain-speci ic languages (DSLs). Speci ically, his enables language designe s o encapsula e he seman ics o pa icula NFPs in a eusable language speci ica ion ha can be wo en in o a base DSL speci ica ion o p oduce a DSL ha also enables he modelling and analysis o ha pa icula NFP in he con ex o a speci ic domain. We ha e p esen ed condi ions o he consis ency o such language componen s; in pa icula hese ensu e ha wea ing a language componen wi h a DSL does no add nei he emo e alid beha iou s om he seman ics o any exp essions in ha DSL. Ou wo k makesa numbe o assump ionsabou he s uc u eo he baseDSL as well as abou he NFPs o be speci ied. In he u u e, we aim o educe hese assump ions o p o ide a mo e gene al amewo k o he speci ica ion o NFPs in DSLs. Mos im- po an ly, we will u he s udy he cases whe e he e is no simple alignmen be ween RlsObs and RlsDSL. 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