This paper presents a manifesto that articulates the conceptual foundations of Agentic Business Process Management (APM), an extension of Business Process Management (BPM) for governing autonomous agents executing processes in organizations. From a management perspective, APM represents a paradigm shift from the traditional view on business processes. This shift is driven by the realization of process awareness by agent-oriented abstractions: software and human agents act as primary functional entities that perceive, reason, and act within explicit process frames. Thus, APM moves away from automation-oriented BPM towards systems in which autonomy is constrained, aligned, and made operational through process aware agents. We introduce the core abstractions and architectural elements required to realize APM systems and elaborate on four key capabilities that agents in APM systems must support: framed autonomy , explainability , conversational actionability , and self-modification . These capabilities jointly ensure that agents’ goals are aligned with organizational goals and that agents behave in a framed yet proactive manner in pursuing those goals. We discuss the extent to which the capabilities can be realized and identify research challenges whose resolution requires further advances in BPM, AI, and multi-agent systems. The manifesto thus serves as a roadmap for bridging these communities and for guiding the development of APM systems in practice.

Code changes can introduce defects that affect software quality and reliability. Just-in-time (JIT) defect prediction techniques provide feedback at check-in time on whether a code change is likely to contain defects. This immediate feedback allows practitioners to make timely decisions regarding potential defects. However, a prediction model may deliver false predictions, that may negatively affect practitioners' decisions. False positive predictions lead to unnecessarily spending resources on investigating clean code changes, while false negative predictions may result in overlooking defective changes. Knowing how uncertain a defect prediction is, would help practitioners to avoid wrong decisions. Previous research in defect prediction explored different approaches to quantify prediction uncertainty for supporting decision-making activities. However, these approaches only offer a heuristic quantification of uncertainty and do not provide guarantees.

Online reinforcement learning (RL) is increasingly used for realizing adaptive systems in the presence of design time uncertainty because Online RL can leverage data only available at run time. With Deep RL gaining interest, the learned knowledge is no longer represented explicitly but hidden in the parameterization of the underlying artificial neural network. For a human, it thus becomes practically impossible to understand the decision-making of Deep RL, which makes it difficult for (1) software engineers to perform debugging, (2) system providers to comply with relevant legal frameworks, and (3) system users to build trust. The explainable RL technique XRL-DINE, introduced in earlier work, provides insights into why certain decisions were made at important time steps. Here, we perform an empirical user study concerning XRL-DINE involving 73 software engineers split into treatment and control groups. The treatment group is given access to XRL-DINE, while the control group is not. We analyze (1) the participants’ performance in answering concrete questions related to the decision-making of Deep RL, (2) the participants’ self-assessed confidence in giving the right answers, (3) the perceived usefulness and ease of use of XRL-DINE, and (4) the concrete usage of the XRL-DINE dashboard.

Assuring high software quality becomes increasingly difficult as software systems become more and more complex and continuously grow in size. Moreover, testing becomes even more expensive when dealing with large-scale systems. Thus, to effectively allocate quality assurance resources, researchers have proposed fault prediction (FP) which utilizes machine learning (ML) to predict fault-prone code areas. However, ML algorithms typically make use of stochastic elements to increase the prediction models’ generalizability and efficiency of the training process. These stochastic elements, also known as nondeterminism-introducing (NI) factors, lead to variance in the training process and as a result, lead to variance in prediction accuracy and training time. This variance poses a challenge for reproducing research results. More importantly, while fault prediction models may have shown good performance in the lab (e.g., often-times involving multiple runs and averaging outcomes), high variance of results can pose the risk that these models show low performance when applied in practice. In this work, we experimentally analyze the variance of a state-of-the-art fault prediction approach. Our experimental results indicate that NI factors can indeed cause considerable variance in the fault prediction models’ accuracy. We observed a maximum variance of 10.10% in terms of the per-class accuracy metric. We thus, also discuss how to deal with such variance.

An edge data center can host applications that require low-latency access to nearby end devices. If the resource requirements of the applications exceed the capacity of the edge data center, some non-latency-critical application components may be offloaded to the cloud. Such offloading may incur financial costs both for the use of cloud resources and for data transfer between the edge data center and the cloud. Moreover, such offloading may violate data protection requirements if components process sensitive data. The operator of the edge data center has to decide which components to keep in the edge data center and which ones to offload to the cloud. In this paper, we formalize this problem and prove that it is strongly NP-hard. We introduce an optimization algorithm that is fast enough to be run online for dynamic and automatic offloading decisions, guarantees that the solution satisfies hard constraints regarding latency, data protection, and capacity, and achieves near-optimal costs. We also show how the algorithm can be extended to handle multiple edge data centers. Experiments show that the cost of the solution found by our algorithm is on average only 2.7% higher than the optimum.

Abstract Proactive process adaptation can prevent and mitigate upcoming problems during process execution. Proactive adaptation decisions are based on predictions about how an ongoing process instance will unfold up to its completion. On the one hand, these predictions must have high accuracy, as, for instance, false negative predictions mean that necessary adaptations are missed. On the other hand, these predictions should be produced early during process execution, as this leaves more time for adaptations, which typically have non-negligible latencies. However, there is an important tradeoff between prediction accuracy and earliness. Later predictions typically have a higher accuracy, because more information about the ongoing process instance is available. To address this tradeoff, we use an ensemble of deep learning models that can produce predictions at arbitrary points during process execution and that provides reliability estimates for each prediction. We use these reliability estimates to dynamically determine the earliest prediction with sufficient accuracy, which is used as basis for proactive adaptation. Experimental results indicate that our dynamic approach may offer cost savings of 27% on average when compared to using a static prediction point.

The design and engineering of collaborative networks and business ecosystems is a discipline that requires an outstanding and upfront attention of the value cogenerated among the parties involved in the business exchanges of these networks. Understanding this value cocreation is undoubtedly paramount, first to adequately sustain the design and the development of the information system that brings about this value, second, to support the communication between the information system designers, and third to allow discovering new cocreation opportunities amongst the networks companies. In that context, we proposed an abstract language (metamodel) that structures, and provides an explanatory semantics to, the cocreation of value between information system designers, allowing a better definition of the collaboration and of each one value propositions. The design of this language is achieved in the frame of the design science theory and accordingly follows an iterative improvement approach based on real case studies from practitioners. This paper introduces the second iteration of the language based on a real case in a Smart Airport Network.

Predictive business process monitoring aims to predict how an ongoing process instance will unfold up to its completion, thereby facilitating proactively responding to anticipated problems. Recurrent Neural Networks (RNNs), a special form of deep learning techniques, gain interest as a prediction technique in BPM. However, non-sequential control flows may make the prediction task more difficult, because RNNs were conceived for learning and predicting sequences of data. Based on an industrial dataset, we provide experimental results comparing different alternatives for considering non-sequential control flows. In particular, we consider cycles and parallelism for business process prediction with RNNs.

Complex Event Processing (CEP) facilitates monitoring large-scale, distributed systems. CEP applications analyze real-time streams of events to detect patterns that indicate problems that may require an adaptation of the running system. Like for any software system, developers may introduce faults when designing and implementing CEP applications. Such faults may imply that true problems may not be detected during systems operation, or false alarms may be raised even though no problem exists. Therefore, verifying the CEP application during design time is critical to ensure correct system monitoring at run-time. To address the scalability problem of verifying CEP applications, we propose an incremental verification approach building on recent advances in model checking. Results of an initial evaluation indicate under which assumptions our approach scales better than standard model checking.

Protecting sensitive data is a key concern for the adoption of cloud solutions. Protecting data in the cloud is made particularly challenging by the dynamic changes that cloud systems may undergo at run-time, as well as the complex interactions among multiple software and hardware components, services, and stakeholders. Conformance to data protection requirements in such a dynamic environment cannot any longer be ensured during design time; e.g. due to the dynamic changes imposed by replication and migration of components. It requires run-time data protection mechanisms. This paper proposes combining multiple existing data protection approaches and extending them to run-time, ultimately delivering an end-to-end architecture for run-time data protection in the cloud. We validate the practical applicability of our approach by a commercial case study.

Abstract Predictive business process monitoring aims at predicting potential problems during process execution so that these problems can be proactively managed and mitigated. Compared to aggregate prediction accuracy indicators (e.g., precision or recall), prediction reliability estimates provide additional information about the prediction error for an individual business process. Intuitively, it appears appealing to consider reliability estimates when deciding on whether to adapt a running process instance or not. However, we lack empirical evidence to support this intuition, as research on predictive business process monitoring focused on aggregate prediction accuracy. We experimentally analyze the effect of considering prediction reliability estimates for proactive business process adaptation. We use ensemble prediction techniques, which we apply to an industry data set from the transport and logistics domain. In our experiments, proactive business process adaptation in general had a positive effect on cost in 52.5% of the situations. In 82.9% of these situations, considering reliability estimates increased the positive effect, leading to cost savings of up to 54%, with 14% savings on average.

Concerns about protecting personal data and intellectual property are major obstacles to the adoption of cloud services. To ensure that a cloud tenant's data cannot be accessed by malicious code from another tenant, critical software components of different tenants are traditionally deployed on separate physical machines. However, such physical separation limits hardware utilization, leading to cost overheads due to inefficient resource usage. Secure hardware enclaves offer mechanisms to protect code and data from potentially malicious code deployed on the same physical machine, thereby offering an alternative to physical separation. We show how secure hardware enclaves can be employed to address data protection concerns of cloud tenants, while optimizing hardware utilization. We provide a model, formalization and experimental evaluation of an efficient algorithmic approach to compute an optimized deployment of software components and virtual machines, taking into account data protection concerns and the availability of secure hardware enclaves. Our experimental results suggest that even if only a small percentage of the physical machines offer secure hardware enclaves, significant cost savings can be achieved.

Abstract Proactive process adaptation facilitates preventing or mitigating upcoming problems during process execution, such as process delays. Key for proactive process adaptation is that adaptation decisions are based on accurate predictions of problems. Previous research focused on improving aggregate accuracy, such as precision or recall. However, aggregate accuracy provides little information about the error of an individual prediction. In contrast, so called reliability estimates provide such additional information. Previous work has shown that considering reliability estimates can improve decision making during proactive process adaptation and can lead to cost savings. So far, only constant cost functions have been considered. In practice, however, costs may differ depending on the magnitude of the problem; e.g., a longer process delay may result in higher penalties. To capture different cost functions, we exploit numeric predictions computed from ensembles of regression models. We combine reliability estimates and predicted costs to quantify the risk of a problem, i.e., its probability and its severity. Proactive adaptations are triggered if risks are above a pre-defined threshold. A comparative evaluation indicates that cost savings of up to 31%, with 14.8% savings on average, may be achieved by the risk-based approach.

Transport and logistics faces fluctuations in cargo volume that statistically can only be captured with a large error. Observing and managing such dynamic volume fluctuations more effectively promises many benefits such as reducing unused transport capacity and ensuring timely delivery of cargo. This paper introduces an approach that combines user-friendly mobile devices with internet-connected sensors to deliver up-to-date, timely, and precise information about parcel volumes inside containers. In particular, we present (1) RCM, a mobile app for unique identification of containers, and (2) SNAP, a novel approach for employing internet-connected low-cost, off-the-shelf 3D scanners for capturing and analyzing actual cargo volumes. We have evaluated the accuracy of SNAP in controlled experiments indicating that cargo volume can be measured with high accuracy. We have further evaluated RCM together with SNAP by means of a survey study with domain experts, revealing its high potential for practical use.

Big Data is about extracting valuable information from data in order to use it in intelligent ways such as to revolutionize decision-making in businesses, science and society. BIGDSE 2015 discusses the link between Big Data and software engineering and critically looks into issues such as cost-benefit of big data.

Predictive business process monitoring aims at forecasting potential problems during process execution before they occur so that these problems can be handled proactively. Several predictive monitoring techniques have been proposed in the past. However, so far those prediction techniques have been assessed only independently from each other, making it hard to reliably compare their applicability and accuracy. We empirically analyze and compare three main classes of predictive monitoring techniques, which are based on machine learning, constraint satisfaction, and Quality-of-Service (QoS) aggregation. Based on empirical evidence from an industrial case study in the area of transport and logistics, we assess those techniques with respect to five accuracy indicators. We further determine the dependency of accuracy on the point in time during process execution when a prediction is made in order to determine lead-times for accurate predictions. Our evidence suggests that, given a lead-time of half of the process duration, all predictive monitoring techniques consistently provide an accuracy of at least 70%. Yet, it also becomes evident that the techniques differ in terms of how accurately they may predict violations and nonviolations. To improve the prediction process, we thus exploit the characteristics of the individual techniques and propose their combination. Based on our case study data, evidence indicates that certain combinations of techniques may outperform individual techniques with respect to specific accuracy indicators. Combining constraint satisfaction with QoS aggregation, for instance, improves precision by 14%; combining machine learning with constraint satisfaction shows an improvement in recall by 23%.

The transition to cloud computing offers a large number of benefits, such as lower capital costs and a highly agile environment. Yet, the development of software engineering practices has not kept pace with this change. Moreover, the design and runtime behavior of cloud based services and the underlying cloud infrastructure are largely decoupled from one another.This paper describes the innovative concepts being developed by CloudWave to utilize the principles of DevOps to create an execution analytics cloud infrastructure where, through the use of programmable monitoring and online data abstraction, much more relevant information for the optimization of the ecosystem is obtained. Required optimizations are subsequently negotiated between the applications and the cloud infrastructure to obtain coordinated adaption of the ecosystem. Additionally, the project is developing the technology for a Feedback Driven Development Standard Development Kit which will utilize the data gathered through execution analytics to supply developers with a powerful mechanism to shorten application development cycles.

Increasingly, service-based applications (SBAs) are composed of third-party services available over the Internet. Even if third-party services have shown to work during design-time, they might fail during the operation of the SBA due to changes in their implementation, provisioning, or the communication infrastructure. As a consequence, SBAs need to dynamically adapt to such failures during run-time to ensure that they maintain their expected functionality and quality. Ideally the need for an adaptation is proactively identified, i.e., failures are predicted before they can lead to consequences such as costly compensation and roll-back activities. Currently, approaches to predict failures are based on monitoring. Due to its passive nature, however, monitoring might not cover all relevant service executions, which can diminish the ability to correctly predict failures. In this paper we demonstrate how online testing, as an active approach, can improve failure prediction by considering a broader range of service executions. Specifically, we introduce a framework and prototypical implementation that exploits synergies between monitoring, online testing and quality prediction. For online test selection and assessment we adapt usage-based testing strategies. We experimentally evaluate the strengths of our approach in predicting the need for an adaptation of an SBA.

Service-oriented systems have attracted great interest from industry and research communities worldwide. Service integrators, developers, and providers are collaborating to address the various challenges in the field. PESOS 2010 is a forum for all these communities to present and discuss a wide range of topics related to service-oriented systems. The goal of PESOS was to bring together researchers from academia and industry, as well as practitioners working in the areas of software engineering and service-oriented systems to discuss research challenges, recent developments, novel applications, as well as methods, techniques, experiences, and tools to support the engineering and use of service-oriented systems.

Software product line engineering (SPLE) has shown to be a very successful paradigm for developing a diversity of similar software products at low cost, in short time, and with high quality.

Feature diagrams are a popular means for documenting variability in software product line engineering. When examining feature diagrams in the literature and from industry, we observed that the same modelling concepts are used for documenting two different kinds of variability: (1) product line variability, which reflects decisions of product management on how the systems that belong to the product line should vary, and (2) software variability, which reflects the ability of the reusable product line artefacts to be customized or configured. To disambiguate the documentation of variability, we follow previous suggestions to relate orthogonal variability models (OVMs) to feature diagrams. This paper reuses an existing formalization of feature diagrams, but introduces a formalization of OVMs. Then, the relationships between the two kinds of models are formalized as well. Besides a precise definition of the languages and the links, the important benefit of this formalization is that it serves as a foundation for a tool supporting automated reasoning on variability. This tool can, e.g., analyse whether the product line artefacts are flexible enough to build all the systems that should belong to the product line.

By explicitly modeling and managing variability, software product line engineering provides a systematic approach for creating a diversity of similar products at low cost, in short time, and with high quality. This tutorial focuses on the two principle differences of software product line engineering when compared to single systems development: The differentiation of two key development processes (domain engineering and application engineering) and the explicit representation and management of variability. We characterize the two processes and their main activities and introduce the orthogonal variability modeling approach (OVM). We further illustrate the OVM approach in the product line requirements engineering and product line testing activities.

Software product line engineering (SPLE) is the approach for creating a diversity of similar products at low cost, in short time, and with high quality. Explicitly documenting product line variability is essential for variability management as it significantly supports the following activities: Defining the commonality and the variability of the product line during domain engineering; Realizing reusable artifacts (the domain artifacts) with variability; Defining the binding of variability during application engineering; Deriving individual applications by exploiting the variability in the domain artifacts. This tutorial is based on our text book on software product line engineering [1]. The tutorial is structured along an SPLE framework, which has been defined based on our experiences and the results of the European software product line research projects ESAPS, CAFÉ, and FAMILIES [2].