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Ecosystem Emergence and Founding Conditions - Lessions Learned from an Imprinting Perspective
(2022)
The rise of ecosystem prominence has provided several definitions of how we understand ecosystems nowadays. In this context, several scholars have considered influencing factors for ecosystem emergence. This paper addresses this consideration and analyzes the salient characteristics of different ecosystem types and their potential persistence since ecosystem founding to improve the understanding of emergence. We applied a three-step approach (1) identifying ecosystem types based on bibliometric analysis, (2) exploring salient characteristics per ecosystem type using qualitative content analysis and (3) deriving founding conditions from the salient characteristics following a conceptual approach. Based on a bibliometric analysis, we identified business/innovation, entrepreneurial and service ecosystems. In a second step, we developed salient characteristics within the themes of structure, power constellation/interdependencies and governance by inductive coding. As we identified a significant difference in alignment structure, we analyzed if alignment structure persists since ecosystem origin and explains why ecosystems differ. We analyzed potential pairings between alignment structure and their respective founding condition for every ecosystem type. With the alignment structures’ persistence, we can better understand why ecosystem types differ.
Automated regression tests are a key enabler for applying popular continuous software engineering techniques. This paper focuses on testing BPMN-based Process-Driven Applications (PDA). When evolving PDAs, the affected test cases must be identified and co-evolved as well. In this process, affected test cases can be overlooked, misunderstandings may occur during communication between different roles involved, and implementation errors can arise. Regardless of possible error sources, the entire test migration process is time-consuming. This paper presents a new semi-automated test migration process for PDAs. The concept builds on previous work on creating regression tests using a no-code approach. Our approach identifies the modifications of the PDA and classifies their impact on previously defined tests. The classification indicates whether existing test code can be migrated automatically or whether a manual revision becomes necessary. During an AB/BA experiment, the concept and the developed prototype proved a more efficient test migration process and a higher test quality.
Process-Driven Applications flourish through the interaction between an executable BPMN process model, human tasks, and external software services. All these components operate on shared process data, so it is even more important to check the correct data flow. However, data flow is in most cases not explicitly defined but hidden in model elements, form declarations, and program code. This paper elaborates on data-flow anomalies acting as indicators for potential errors and how such anomalies can be uncovered despite implicit and hidden data-flow definitions. By considering an integrated view, it goes beyond other approaches which are restricted to separate data-flow analysis of either process model or source code. The main idea is to merge call graphs representing programmed services into a control-flow representation of the process model, to label the resulting graph with associated data operations, and to detect anomalies in that labeled graph using a dedicated data-flow analysis. The applicability of the solution is demonstrated by a prototype designed for the Camunda BPM platform.