arXiv subjects

Christoph Matthies

Publications and source records attributed to Christoph Matthies.

At least 19 recordsLinked to original sources

arXiv2023

From Full-fledged ERP Systems Towards Process-centric Business Process Platforms

Enterprise Resource Planning (ERP) systems are critical to the success of enterprises, facilitating business operations through standardized digital processes. However, existing ERP systems are unsuitable for startups and small and medium-sized enterprises that grow quickly and require adaptable solutions with low barriers to entry. Drawing upon 15 explorative interviews with industry experts, we examine the challenges of current ERP systems using the task technology fit theory across companies of varying sizes. We describe high entry barriers, high costs of implementing implicit processes, and insufficient interoperability of already employed tools. We present a vision of a future business process platform based on three enablers: Business processes as first-class entities, semantic data and processes, and cloud-native elasticity and high availability. We discuss how these enablers address current ERP systems' challenges and how they may be used for research on the next generation of business software for tomorrow's enterprises.

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arXiv2022

"Communication Is a Scarce Resource!'': A Summary of CHASE'22 Conference Discussions

Background: Software Engineering regularly views communication between project participants as a tool for solving various problems in software development. Objective: Formulate research questions in areas related to CHASE. Method: A day-long discussion of five participants at the in-person day of the 15th International Conference on Cooperative and Human Aspects of Software Engineering (CHASE 2022) on May 23rd 2022. Results: It is not rare in industrial SE projects that communication is not just a tool or technique to be applied but also represents a resource, which, when lacking, threatens project success. This situation might arise when a person required to make decisions (especially regarding requirements, budgets, or priorities) is often unavailable. It may be helpful to frame communication as a scarce resource to understand the key difficulty of such situations. Conclusion: We call for studies that focus on the allocation and management of scarce communication resources of stakeholders as a lens to analyze software engineering projects.

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arXiv2021

Joining Forces: Applying Design Thinking Techniques in Scrum Meetings

The most prominent Agile framework Scrum, is often criticized for its amount of meetings. These regular events are essential to the empirical inspect-and-adapt cycle proposed by Agile methods. Scrum meetings face several challenges, such as being perceived as boring, repetitive, or irrelevant, leading to decreased cooperation in teams and less successful projects. In an attempt to address these challenges, Agile practitioners have adopted teamwork, innovation, and design techniques geared towards improving collaboration. Additionally, they have developed their own activities to be used in Scrum meetings, most notably for conducting retrospective and planning events. Design thinking incorporates non-designers and designers in design and conceptualization activities, including user research, ideation, or testing. Accordingly, the design thinking approach provides a process with different phases and accompanying techniques for each step. These design thinking techniques can support shared understanding in teams and can improve collaboration, creativity, and product understanding. For these reasons, design thinking techniques represent a worthwhile addition to the Scrum meeting toolkit and can support Agile meetings in preventing or countering common meeting challenges and achieving meeting goals. This chapter explores how techniques from the design thinking toolkit can support Scrum meetings from a theoretical and practical viewpoint. We analyze Scrum meetings' requirements, goals, and challenges and link them to groups of techniques from the design thinking toolkit. In addition, we review interview and observational data from two previous studies with software development practitioners and derive concrete examples. As a result, we present initial guidelines on integrating design thinking techniques into Scrum meetings to make them more engaging, collaborative, and interactive.

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arXiv2021

ESPBench: The Enterprise Stream Processing Benchmark

Growing data volumes and velocities in fields such as Industry 4.0 or the Internet of Things have led to the increased popularity of data stream processing systems. Enterprises can leverage these developments by enriching their core business data and analyses with up-to-date streaming data. Comparing streaming architectures for these complex use cases is challenging, as existing benchmarks do not cover them. ESPBench is a new enterprise stream processing benchmark that fills this gap. We present its architecture, the benchmarking process, and the query workload. We employ ESPBench on three state-of-the-art stream processing systems, Apache Spark, Apache Flink, and Hazelcast Jet, using provided query implementations developed with Apache Beam. Our results highlight the need for the provided ESPBench toolkit that supports benchmark execution, as it enables query result validation and objective latency measures.

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arXiv2021

How Fast Can We Insert? An Empirical Performance Evaluation of Apache Kafka

Message brokers see widespread adoption in modern IT landscapes, with Apache Kafka being one of the most employed platforms. These systems feature well-defined APIs for use and configuration and present flexible solutions for various data storage scenarios. Their ability to scale horizontally enables users to adapt to growing data volumes and changing environments. However, one of the main challenges concerning message brokers is the danger of them becoming a bottleneck within an IT architecture. To prevent this, knowledge about the amount of data a message broker using a specific configuration can handle needs to be available. In this paper, we propose a monitoring architecture for message brokers and similar Java Virtual Machine-based systems. We present a comprehensive performance analysis of the popular Apache Kafka platform using our approach. As part of the benchmark, we study selected data ingestion scenarios with respect to their maximum data ingestion rates. The results show that we can achieve an ingestion rate of about 420,000 messages/second on the used commodity hardware and with the developed data sender tool.

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arXiv2021

Experience vs Data: A Case for More Data-informed Retrospective Activities

Effective Retrospective meetings are vital for ensuring productive development processes because they provide the means for Agile software development teams to discuss and decide on future improvements of their collaboration. Retrospective agendas often include activities that encourage sharing ideas and motivate participants to discuss possible improvements. The outcomes of these activities steer the future directions of team dynamics and influence team happiness. However, few empirical evaluations of Retrospective activities are currently available. Additionally, most activities rely on team members experiences and neglect to take existing project data into account. With this paper we want to make a case for data-driven decision-making principles, which have largely been adopted in other business areas. Towards this goal we review existing retrospective activities and highlight activities that already use project data as well as activities that could be augmented to take advantage of additional, more subjective data sources. We conclude that data-driven decision-making principles, are advantageous, and yet underused, in modern Agile software development. Making use of project data in retrospective activities would strengthen this principle and is a viable approach as such data can support the teams in making decisions on process improvement.

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arXiv2020

Mining for Process Improvements: Analyzing Software Repositories in Agile Retrospectives

Software Repositories contain knowledge on how software engineering teams work, communicate, and collaborate. It can be used to develop a data-informed view of a team's development process, which in turn can be employed for process improvement initiatives. In modern, Agile development methods, process improvement takes place in Retrospective meetings, in which the last development iteration is discussed. However, previously proposed activities that take place in these meetings often do not rely on project data, instead depending solely on the perceptions of team members. We propose new Retrospective activities, based on mining the software repositories of individual teams, to complement existing approaches with more objective, data-informed process views.

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arXiv2020

More than Code: Contributions in Scrum Software Engineering Teams

Motivated and competent team members are a vital part of Agile Software development and make or break any project's success. Motivation is fostered by continuous progress and recognition of efforts. These concepts are founding pillars of the Scrum methodology, which focuses on self-organizing teams. The types of contributions Scrum development team members make to a project's progress are not only technical. However, a comprehensive model comprising the varied contributions in modern software engineering teams is not yet established. We propose a model that incorporates contributions of all Scrum roles, explicitly including those which are not directly related to project artifacts. It improves the visibility of performed tasks, acts as a starting point for team retrospection, and serves as a foundation for discussion in the research community.

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arXiv2020

Attitudes, Beliefs, and Development Data Concerning Agile Software Development Practices

The perceptions and attitudes of developers impact how software projects are run and which development practices are employed in development teams. Recent agile methodologies have taken this into account, focusing on collaboration and shared team culture. In this research, we investigate the perceptions of agile development practices and their usage in Scrum software development teams. Although perceptions collected through surveys of 42 participating students did not evolve significantly over time, our analyses show that the Scrum role significantly impacted participants' views of employed development practices. We find that using the version control system according to agile ideas was consistently rated most related to the values of the Agile Manifesto. Furthermore, we investigate how common software development artifacts can be used to gain insights into team behavior and present the development data measurements we employed. We show that we can reliably detect well-defined agile practices, such Test-Driven Development, in this data and that usage of these practices coincided with participants' self-assessments.

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arXiv2020

Towards Empirically Validated Remedies for Scrum Retrospective Headaches

Agile methods, especially Scrum, have become staples of the modern software development industry. Retrospective meetings are Scrum's instrument for process improvement and adaptation. They are considered one of the most important aspects of the Scrum method and its implementation in organizations. However, Retrospectives face their own challenges. Agile practitioners have highlighted common problems, i.e. headaches, that repeatedly appear in meetings and negatively impact the quality of process improvement efforts. To remedy these headaches, Retrospective activities, which can help teams think together and break the usual routine, have been proposed. In this research, we present case studies of educational and industry teams, investigating the effects of eleven Retrospective activities on five identified headaches. While we find evidence for the claimed benefits of activities in the majority of studied cases, application of remedies also led to new headaches arising.

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arXiv2019

Towards Using Data to Inform Decisions in Agile Software Development: Views of Available Data

Software development comprises complex tasks which are performed by humans. It involves problem solving, domain understanding and communication skills as well as knowledge of a broad variety of technologies, architectures, and solution approaches. As such, software development projects include many situations where crucial decisions must be made. Making the appropriate organizational or technical choices for a given software team building a product can make the difference between project success or failure. Software development methods have introduced frameworks and sets of best practices for certain contexts, providing practitioners with established guidelines for these important choices. Current Agile methods employed in modern software development have highlighted the importance of the human factors in software development. These methods rely on short feedback loops and the self-organization of teams to enable collaborative decision making. While Agile methods stress the importance of empirical process control, i.e. relying on data to make decisions, they do not prescribe in detail how this goal should be achieved. In this paper, we describe the types and abstraction levels of data and decisions within modern software development teams and identify the benefits that usage of this data enables. We argue that the principles of data-driven decision making are highly applicable, yet underused, in modern Agile software development.

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arXiv2019

Application of Data Stream Processing Technologies in Industry 4.0 -- What is Missing?

Industry 4.0 is becoming more and more important for manufacturers as the developments in the area of Internet of Things advance. Another technology gaining more attention is data stream processing systems. Although such streaming frameworks seem to be a natural fit for Industry 4.0 scenarios, their application in this context is still low. The contributions in this paper are threefold. Firstly, we present industry findings that we derived from site inspections with a focus on Industry 4.0. Moreover, our view on Industry 4.0 and important related aspects is elaborated. As a third contribution, we illustrate our opinion on why data stream processing technologies could act as an enabler for Industry 4.0 and point out possible obstacles on this way.

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arXiv2019

Quantitative Impact Evaluation of an Abstraction Layer for Data Stream Processing Systems

With the demand to process ever-growing data volumes, a variety of new data stream processing frameworks have been developed. Moving an implementation from one such system to another, e.g., for performance reasons, requires adapting existing applications to new interfaces. Apache Beam addresses these high substitution costs by providing an abstraction layer that enables executing programs on any of the supported streaming frameworks. In this paper, we present a novel benchmark architecture for comparing the performance impact of using Apache Beam on three streaming frameworks: Apache Spark Streaming, Apache Flink, and Apache Apex. We find significant performance penalties when using Apache Beam for application development in the surveyed systems. Overall, usage of Apache Beam for the examined streaming applications caused a high variance of query execution times with a slowdown of up to a factor of 58 compared to queries developed without the abstraction layer. All developed benchmark artifacts are publicly available to ensure reproducible results.

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arXiv2019

Adding Value by Combining Business and Sensor Data: An Industry 4.0 Use Case

Industry 4.0 and the Internet of Things are recent developments that have lead to the creation of new kinds of manufacturing data. Linking this new kind of sensor data to traditional business information is crucial for enterprises to take advantage of the data's full potential. In this paper, we present a demo which allows experiencing this data integration, both vertically between technical and business contexts and horizontally along the value chain. The tool simulates a manufacturing company, continuously producing both business and sensor data, and supports issuing ad-hoc queries that answer specific questions related to the business. In order to adapt to different environments, users can configure sensor characteristics to their needs.

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arXiv2019

What Stays in Mind? - Retention Rates in Programming MOOCs

This work presents insights about the long-term effects and retention rates of knowledge acquired within MOOCs. In 2015 and 2017, we conducted two introductory MOOCs on object-oriented programming in Java with each over 10,000 registered participants. In this paper, we analyze course scores, quiz results and self-stated skill levels of our participants. The aim of our analysis is to uncover factors influencing the retention of acquired knowledge, such as time passed or knowledge application, in order to improve long-term success. While we know that some participants learned the programming basics within our course, we lack information on whether this knowledge was applied and fortified after the course's end. To fill this knowledge gap, we conducted a survey in 2018 among all participants of our 2015 and 2017 programming MOOCs. The first part of the survey elicits responses on whether and how MOOC knowledge was applied and gives participants opportunity to voice individual feedback. The second part of the survey contains several questions of increasing difficulty and complexity regarding course content in order to learn about the consolidation of the acquired knowledge. We distinguish three programming knowledge areas in the survey: First, understanding of concepts, such as loops and boolean algebra. Second, syntax knowledge, such as specific keywords. Third, practical skills including debugging and coding. We further analyzed the long-term effects separately per participant skill group. While answer rates were low, the collected data shows a decrease of knowledge over time, relatively unaffected by skill level. Application of the acquired knowledge improves the memory retention rates of MOOC participants across all skill levels.

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arXiv2019

Beyond Surveys: Analyzing Software Development Artifacts to Assess Teaching Efforts

This Innovative Practice Full Paper presents an approach of using software development artifacts to gauge student behavior and the effectiveness of changes to curriculum design. There is an ongoing need to adapt university courses to changing requirements and shifts in industry. As an educator it is therefore vital to have access to methods, with which to ascertain the effects of curriculum design changes. In this paper, we present our approach of analyzing software repositories in order to gauge student behavior during project work. We evaluate this approach in a case study of a university undergraduate software development course teaching agile development methodologies. Surveys revealed positive attitudes towards the course and the change of employed development methodology from Scrum to Kanban. However, surveys were not usable to ascertain the degree to which students had adapted their workflows and whether they had done so in accordance with course goals. Therefore, we analyzed students' software repository data, which represents information that can be collected by educators to reveal insights into learning successes and detailed student behavior. We analyze the software repositories created during the last five courses, and evaluate differences in workflows between Kanban and Scrum usage.

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arXiv2019

An Additional Set of (Automated) Eyes: Chatbots for Agile Retrospectives

Recent advances in natural-language processing and data analysis allow software bots to become virtual team members, providing an additional set of automated eyes and additional perspectives for informing and supporting teamwork. In this paper, we propose employing chatbots in the domain of software development with a focus on supporting analyses and measurements of teams' project data. The software project artifacts produced by agile teams during regular development activities, e.g. commits in a version control system, represent detailed information on how a team works and collaborates. Analyses of this data are especially relevant for agile retrospective meetings, where adaptations and improvements to the executed development process are discussed. Development teams can use these measurements to track the progress of identified improvement actions over development iterations. Chatbots provide a convenient user interface for interacting with the outcomes of retrospectives and the associated measurements in a chat-based channel that is already being employed by team members.

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arXiv2018

Should I Bug You? Identifying Domain Experts in Software Projects Using Code Complexity Metrics

In any sufficiently complex software system there are experts, having a deeper understanding of parts of the system than others. However, it is not always clear who these experts are and which particular parts of the system they can provide help with. We propose a framework to elicit the expertise of developers and recommend experts by analyzing complexity measures over time. Furthermore, teams can detect those parts of the software for which currently no, or only few experts exist and take preventive actions to keep the collective code knowledge and ownership high. We employed the developed approach at a medium-sized company. The results were evaluated with a survey, comparing the perceived and the computed expertise of developers. We show that aggregated code metrics can be used to identify experts for different software components. The identified experts were rated as acceptable candidates by developers in over 90% of all cases.

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