Investigating Inconsistency Understanding to Support Interactive Inconsistency Resolution in Declarative Process Models

Handling inconsistencies in business rules is an important part of corporate compliance management. This includes the resolution of inconsistencies, which currently is a fully automated process that might not always be plausible in a real-world scenario. To include human experts and develop interactive resolution approaches, an understanding of inconsistencies is crucial. Thus, we focus on investigating inconsistency understanding in declarative process models by testing the applicability of insights from declarative process model understanding to different inconsistency characteristics. In the future, this will provide the basis for a series of cognitive experiments evaluating the effects of inconsistency characteristics and representation on inconsistency understanding in declarative process models

Discovering hidden dependencies in constraint-based declarative process models for improving understandability

Flexible systems and services require a solid approach for modeling and enacting dynamic behavior. Declarative process models gained plenty of traction lately as they have proven to provide a good fit for the problem at hand, i.e. visualizing and executing flexible business processes. These models are based on constraints that impose behavioral restrictions on process behavior. Essentially, a declarative model is a set of constraints defined over the set of activities in a process. While allowing for very flexible process specifications, a major downside is that the combination of constraints can lead to behavioral restrictions not explicitly visible when reading a model. These restrictions, so-called hidden dependencies, make the models much more difficult to understand. This paper presents a technique for discovering hidden dependencies and making them explicit by means of dependency structures. Experiments with novice process modelers demonstrate that the proposed technique lowers the cognitive effort necessary to comprehend a constraint-based process model.status: publishe

Collaborative Business Process Management - A Literature-based Analysis of Methods for Supporting Model Understandability

Due to the growing amount of cooperative business scenarios, collaborative Business Process Management (cBPM) has emerged. The increased number of stakeholders with minor expertise in process modeling leads to a high relevance of model understandability in cBPM contexts. Despite extensive works in the research fields of cBPM and model understandability in BPM, there is no analysis and comprehensive overview of methods supporting process model understandability in cBPM scenarios. To address this research gap, this paper presents the results of a literature review. The paper identifies concepts for supporting model understandability in BPM, provides an overview of methods implementing these concepts, and discusses the methods’ applicability in cBPM. The four concepts process model transformation, process model visualization, process model description, and modeling support are introduced. Subsequently, 69 methods are classified and discussed in the context of cBPM. Results contribute to revealing existing academic voids and can guide practitioners in cBPM scenarios

Language-independent look-ahead for checking multi-perspective declarative process models

Declarative process modelling languages focus on describing a process by restrictions over the behaviour, which must be satisfied throughout the whole process execution. Hence, they are well suited for modelling knowledge-intensive processes with many decision points. However, such models can be hard to read and understand, which affect the modelling and maintenance of the process models tremendously as well as their execution. When executing such declarative (multi-perspective) process models, it may happen that the execution of activities or the change of data values may result in the non-executability of crucial activities. Hence, it would be beneficial to know all consequences of decisions to give recommendations to the process participants. A look-ahead attempts to predict the effects of executing an activity towards possible consequences within an a priori defined time window. The prediction is based on the current state of the process execution, the intended next event and the underlying process model. While execution engines for single-perspective imperative process models already implement such functionality, execution approaches, for multi-perspective declarative process models that involve constraints on data and resources, are less mature. In this paper, we introduce a simulation-based look-ahead approach for multi-perspective declarative process models. This approach transforms the problem of a context-aware process simulation into a SAT problem, by translating a declarative multi-perspective process model and the current state of a process execution into a specification of the logic language Alloy. Via a SAT solver, process trajectories are generated that either satisfy or violate this specification. The simulated process trajectories are used to derive consequences and effects of certain decisions at any time of process execution. We evaluate our approach by means of three examples and give some advice for further optimizations

Design principles for ensuring compliance in business processes

In this thesis, we evaluate the complexity and understandability of compliance languages. First, to calculate the complexity, we apply established software metrics and interpret the results with respect to the languages’ expressiveness. Second, to investigate the languages’ understandability, we use a cognitive model of the human problem-solving process and analyze how efficiently users perform a compliance modeling task. Our results have theoretical and practical implications that give directions for the development of compliance languages, and rule-based languages in general.Diese Arbeit beurteilt die Komplexität und Verständlichkeit von Compliance-Sprachen. Zur Messung der Komplexität wenden wir etablierte Software-Metriken an und interpretieren die Ergebnisse in Hinblick auf die Aussagekraft der Sprachen. Zur Untersuchung der Verständlichkeit verwenden wir ein kognitives Modell und analysieren, wie effizient eine Compliance-Sprache zur Lösung eines Modellierungsproblems eingesetzt wird. Unsere Ergebnisse haben theoretische und praktische Implikationen für die Entwicklung von Compliance-Sprachen und anderen regelbasierten Sprachen

Model Transformation Languages with Modular Information Hiding

Model transformations, together with models, form the principal artifacts in model-driven software development. Industrial practitioners report that transformations on larger models quickly get sufficiently large and complex themselves. To alleviate entailed maintenance efforts, this thesis presents a modularity concept with explicit interfaces, complemented by software visualization and clustering techniques. All three approaches are tailored to the specific needs of the transformation domain

Model Transformation Languages with Modular Information Hiding

Model transformations, together with models, form the principal artifacts in model-driven software development. Industrial practitioners report that transformations on larger models quickly get sufficiently large and complex themselves. To alleviate entailed maintenance efforts, this thesis presents a modularity concept with explicit interfaces, complemented by software visualization and clustering techniques. All three approaches are tailored to the specific needs of the transformation domain

Resolving inconsistencies and redundancies in declarative process models

Declarative process models define the behaviour of business processes as a set of constraints. Declarative process discovery aims at inferring such constraints from event logs. Existing discovery techniques verify the satisfaction of candidate constraints over the log, but completely neglect their interactions. As a result, the inferred constraints can be mutually contradicting and their interplay may lead to an inconsistent process model that does not accept any trace. In such a case, the output turns out to be unusable for enactment, simulation or verification purposes. In addition, the discovered model contains, in general, redundancies that are due to complex interactions of several constraints and that cannot be cured using existing pruning approaches. We address these problems by proposing a technique that automatically resolves conflicts within the discovered models and is more powerful than existing pruning techniques to eliminate redundancies. First, we formally define the problems of constraint redundancy and conflict resolution. Second, we introduce techniques based on the notion of automata-product monoid, which guarantees the consistency of the discovered models and, at the same time, keeps the most interesting constraints in the pruned set. The level of interestingness is dictated by user-specified prioritisation criteria. We evaluate the devised techniques on a set of real-world event logs

Context-aware Process Management for the Software Engineering Domain

Historically, software development projects are challenged with problems concerning budgets, deadlines and the quality of the produced software. Such problems have various causes like the high number of unplanned activities and the operational dynamics present in this domain. Most activities are knowledge-intensive and require collaboration of various actors. Additionally, the produced software is intangible and therefore difficult to measure. Thus, software producers are often insufficiently aware of the state of their source code, while suitable software quality measures are often applied too late in the project lifecycle, if at all. Software development processes are used by the majority of software companies to ensure the quality and reproducibility of their development endeavors. Typically, these processes are abstractly defined utilizing process models. However, they still need to be interpreted by individuals and be manually executed, resulting in governance and compliance issues. The environment is sufficiently dynamic that unforeseen situations can occur due to various events, leading to potential aberrations and process governance issues. Furthermore, as process models are implemented manually without automation support, they impose additional work for the executing humans. Their advantages often remain hidden as aligning the planned process with reality is cumbersome. In response to these problems, this thesis contributes the Context-aware Process Management (CPM) framework. The latter enables holistic and automated support for software engineering projects and their processes. In particular, it provides concepts for extending process management technology to support software engineering process models in their entirety. Furthermore, CPM contributes an approach to integrate the enactment of the process models better with the real-world process by introducing a set of contextual extensions. Various events occurring in the course of the projects can be utilized to improve process support and activities outside the realm of the process models can be covered. That way, the continuously growing divide between the plan and reality that often occurs in software engineering projects can be avoided. Finally, the CPM framework comprises facilities to better connect the software engineering process with other important aspects and areas of software engineering projects. This includes automated process-oriented support for software quality management or software engineering knowledge management. The CPM framework has been validated by a prototypical implementation, various sophisticated scenarios, and its practical application at two software companies