Process modeling

Process models are processes of the same nature that are classified together into a model. Thus, a process model is a description of a process at the type level. Since the process model is at the type level, a process is an instantiation of it. The same process model is used repeatedly for the development of many applications and thus, has many instantiations. One possible use of a process model is to prescribe how things must/should/could be done in contrast to the process itself which is really what happens. A process model is roughly an anticipation of what the process will look like. What the process shall be will be determined during actual system development. The goals of a process model are to be: • Descriptive • Track what actually happens during a process • Take the point of view of an external observer who looks at the way a process has been performed and determines the improvements that must be made to make it perform more effectively or efficiently. • Prescriptive • Define the desired processes and how they should/could/might be performed. • Establish rules, guidelines, and behavior patterns which, if followed, would lead to the desired process performance. They can range from strict enforcement to flexible guidance. • Explanatory • Provide explanations about the rationale of processes. • Explore and evaluate the several possible courses of action based on rational arguments. • Establish an explicit link between processes and the requirements that the model needs to fulfill. • Pre-defines points at which data can be extracted for reporting purposes. == Purpose ==

From a theoretical point of view, the meta-process modeling explains the key concepts needed to describe what happens in the development process, on what, when it happens, and why. From an operational point of view, the meta-process modeling is aimed at providing guidance for method engineers and application developers. The activity of modeling a business process usually predicates a need to change processes or identify issues to be corrected. This transformation may or may not require IT involvement, although that is a common driver for the need to model a business process. Change management programmes are desired to put the processes into practice. With advances in technology from larger platform vendors, the vision of business process models (BPM) becoming fully executable (and capable of round-trip engineering) is coming closer to reality every day. Supporting technologies include Unified Modeling Language (UML), model-driven architecture, and service-oriented architecture. Process modeling addresses the process aspects of an enterprise business architecture, leading to an all encompassing enterprise architecture. The relationships of a business processes in the context of the rest of the enterprise systems, data, organizational structure, strategies, etc. create greater capabilities in analyzing and planning a change. One real-world example is in corporate mergers and acquisitions; understanding the processes in both companies in detail, allowing management to identify redundancies resulting in a smoother merger. Process modeling has always been a key aspect of business process reengineering, and continuous improvement approaches seen in Six Sigma. == Classification of process models ==

By coverage There are five types of coverage where the term process model has been defined differently: • Activity-oriented: related set of activities conducted for the specific purpose of product definition; a set of partially ordered steps intended to reach a goal. • Product-oriented: series of activities that cause sensitive product transformations to reach the desired product. • Decision-oriented: set of related decisions conducted for the specific purpose of product definition. • Context-oriented: sequence of contexts causing successive product transformations under the influence of a decision taken in a context. • Strategy-oriented: allow building models representing multi-approach processes and plan different possible ways to elaborate the product based on the notion of intention and strategy. By alignment Processes can be of different kinds. By flexibility It was found that while process models were prescriptive, in actual practice departures from the prescription can occur. == Quality of methods ==

As the quality of process models is being discussed in this paper, there is a need to elaborate quality of modeling techniques as an important essence in quality of process models. In most existing frameworks created for understanding the quality, the line between quality of modeling techniques and the quality of models as a result of the application of those techniques are not clearly drawn. This report will concentrate both on quality of process modeling techniques and quality of process models to clearly differentiate the two. Various frameworks were developed to help in understanding quality of process modeling techniques, one example is Quality based modeling evaluation framework or known as Q-Me framework which argued to provide set of well defined quality properties and procedures to make an objective assessment of this properties possible. In short this can make assessment of both the product quality and the process quality of modeling techniques with regard to a set of properties that have been defined before. Quality properties that relate to business process modeling techniques discussed in Authors (Cardoso, Mendling, Neuman and Reijers, 2006) used complexity metrics to measure the simplicity and understandability of a design. This is supported by later research done by Mendling et al. who argued that without using the quality metrics to help question quality properties of a model, simple process can be modeled in a complex and unsuitable way. This in turn can lead to a lower understandability, higher maintenance cost and perhaps inefficient execution of the process in question. The quality of modeling technique is important in creating models that are of quality and contribute to the correctness and usefulness of models. == Quality of models ==

Earliest process models reflected the dynamics of the process with a practical process obtained by instantiation in terms of relevant concepts, available technologies, specific implementation environments, process constraints and so on. Enormous number of research has been done on quality of models but less focus has been shifted towards the quality of process models. Quality issues of process models cannot be evaluated exhaustively however there are four main guidelines and frameworks in practice for such. These are: top-down quality frameworks, bottom-up metrics related to quality aspects, empirical surveys related to modeling techniques, and pragmatic guidelines. Hommes quoted Wang et al. (1994) It defines several quality aspects based on relationships between a model, knowledge Externalisation, domain, a modeling language, and the activities of learning, taking action, and modeling. The framework does not however provide ways to determine various degrees of quality but has been used extensively for business process modeling in empirical tests carried out According to previous research done by Moody et al. with use of conceptual model quality framework proposed by Lindland et al. (1994) to evaluate quality of process model, three levels of quality were identified: • Syntactic quality: Assesses extent to which the model conforms to the grammar rules of modeling language being used. • Semantic quality: whether the model accurately represents user requirements • Pragmatic quality: whether the model can be understood sufficiently by all relevant stakeholders in the modeling process. That is the model should enable its interpreters to make use of it for fulfilling their need. From the research it was noticed that the quality framework was found to be both easy to use and useful in evaluating the quality of process models however it had limitations in regards to reliability and difficult to identify defects. These limitations led to refinement of the framework through subsequent research done by Krogstie. This framework is called SEQUEL framework by Krogstie et al. 1995 (Refined further by Krogstie & Jørgensen, 2002) which included three more quality aspects. • Physical quality: whether the externalized model is persistent and available for the audience to make sense of it. • Empirical quality: whether the model is modeled according to the established regulations regarding a given language. • Social quality: This regards the agreement between the stakeholders in the modeling domain. Dimensions of Conceptual Quality framework Modeling Domain is the set of all statements that are relevant and correct for describing a problem domain, Language Extension is the set of all statements that are possible given the grammar and vocabulary of the modeling languages used. Model Externalization is the conceptual representation of the problem domain. It is defined as the set of statements about the problem domain that are actually made. Social Actor Interpretation and Technical Actor Interpretation are the sets of statements that actors both human model users and the tools that interact with the model, respectively 'think' the conceptual representation of the problem domain contains. Finally, Participant Knowledge is the set of statements that human actors, who are involved in the modeling process, believe should be made to represent the problem domain. These quality dimensions were later divided into two groups that deal with physical and social aspects of the model. In later work, Krogstie et al. The other framework in use is Guidelines of Modeling (GoM) based on general accounting principles include the six principles: Correctness, Clarity deals with the comprehensibility and explicitness (System description) of model systems. Comprehensibility relates to graphical arrangement of the information objects and, therefore, supports the understand ability of a model. Relevance relates to the model and the situation being presented. Comparability involves the ability to compare models that is semantic comparison between two models, Economic efficiency; the produced cost of the design process need at least to be covered by the proposed use of cost cuttings and revenue increases. Since the purpose of organizations in most cases is the maximization of profit, the principle defines the borderline for the modeling process. The last principle is Systematic design defines that there should be an accepted differentiation between diverse views within modeling. Correctness, relevance and economic efficiency are prerequisites in the quality of models and must be fulfilled while the remaining guidelines are optional but necessary. The two frameworks SEQUAL and GOM have a limitation of use in that they cannot be used by people who are not competent with modeling. They provide major quality metrics but are not easily applicable by non-experts. The use of bottom-up metrics related to quality aspects of process models is trying to bridge the gap of use of the other two frameworks by non-experts in modeling but it is mostly theoretical and no empirical tests have been carried out to support their use. Most experiments carried out relate to the relationship between metrics and quality aspects and these works have been done individually by different authors: Canfora et al. study the connection mainly between count metrics (for example, the number of tasks or splits -and maintainability of software process models); Cardoso validates the correlation between control flow complexity and perceived complexity; and Mendling et al. use metrics to predict control flow errors such as deadlocks in process models. The results reveal that an increase in size of a model appears to reduce its quality and comprehensibility. Further work by Mendling et al. investigates the connection between metrics and understanding While some metrics are confirmed regarding their effect, also personal factors of the modeler – like competence – are revealed as important for understanding about the models. Several empirical surveys carried out still do not give clear guidelines or ways of evaluating the quality of process models but it is necessary to have clear set of guidelines to guide modelers in this task. Pragmatic guidelines have been proposed by different practitioners even though it is difficult to provide an exhaustive account of such guidelines from practice. Most of the guidelines are not easily put to practice but "label activities verb–noun" rule has been suggested by other practitioners before and analyzed empirically. From the research. value of process models is not only dependent on the choice of graphical constructs but also on their annotation with textual labels which need to be analyzed. It was found that it results in better models in terms of understanding than alternative labelling styles. From the earlier research and ways to evaluate process model quality it has been seen that the process model's size, structure, expertise of the modeler and modularity affect its overall comprehensibility. Based on these a set of guidelines was presented 7 Process Modeling Guidelines (7PMG). This guideline uses the verb-object style, as well as guidelines on the number of elements in a model, the application of structured modeling, and the decomposition of a process model. The guidelines are as follows: • G1 Minimize the number of elements in a model • G2 Minimize the routing paths per element • G3 Use one start and one end event • G4 Model as structured as possible • G5 Avoid OR routing elements • G6 Use verb-object activity labels • G7 Decompose a model with more than 50 elements 7PMG still though has limitations with its use: Validity problem 7PMG does not relate to the content of a process model, but only to the way this content is organized and represented. It does suggest ways of organizing different structures of the process model while the content is kept intact but the pragmatic issue of what must be included in the model is still left out. The second limitation relates to the prioritizing guideline the derived ranking has a small empirical basis as it relies on the involvement of 21 process modelers only. This could be seen on the one hand as a need for a wider involvement of process modelers' experience, but it also raises the question, what alternative approaches may be available to arrive at a prioritizing guideline? == See also ==