Christoph Matthies

How surveys, tutors, and software help to assess Scrum adoption in a classroom software engineering project

Agile methods are best taught in a hands-on fashion in realistic projects. The main challenge in doing so is to assess whether students apply the methods correctly without requiring complete supervision throughout the entire project. This paper presents experiences from a classroom project where 38 students developed a single system using a scaled version of Scrum. Surveys helped us to identify which elements of Scrum correlated most with student satisfaction or posed the biggest challenges. These insights were augmented by a team of tutors, which accompanied main meetings throughout the project to provide feedback to the teams, and captured impressions of method application in practice. Finally, we performed a post-hoc, tool-supported analysis of collaboration artifacts to detect concrete indicators for anti-patterns in Scrum adoption. Through the combination of these techniques we were able to understand how students implemented Scrum in this course and which elements require further lecturing and tutoring in future iterations. Automated analysis of collaboration artifacts proved to be a promising addition to the development process that could potentially reduce manual efforts in future courses and allow for more concrete and targeted feedback, as well as more objective assessment.

A New Application Benchmark for Data Stream Processing Architectures in an Enterprise Context: Doctoral Symposium

Against the backdrop of ever-growing data volumes and trends like the Internet of Things (IoT) or Industry 4.0, Data Stream Processing Systems (DSPSs) or data stream processing architectures in general receive a greater interest. Continuously analyzing streams of data allows immediate responses to environmental changes. A challenging task in that context is assessing and comparing data stream processing architectures in order to identify the most suitable one for certain settings. The present paper provides an overview about performance benchmarks that can be used for analyzing data stream processing applications. By describing shortcomings of these benchmarks, the need for a new application benchmark in this area, especially for a benchmark covering enterprise architectures, is highlighted. A key role in such an enterprise context is the combination of streaming data and business data, which is barely covered in current data stream processing benchmarks. Furthermore, first ideas towards the development of a solution, i.e., a new application benchmark that is able to fill the existing gap, are depicted.

Agile metrics for a university software engineering course

Teaching agile software development by pairing lectures with hands-on projects has become the norm. This approach poses the problem of grading and evaluating practical project work as well as process conformance during development. Yet, few best practices exist for measuring the success of students in implementing agile practices. Most university courses rely on observations during the course or final oral exams. In this paper, we propose a set of metrics which give insights into the adherence to agile practices in teams. The metrics identify instances in development data, e.g. commits or user stories, where agile processes were not followed. The identified violations can serve as starting points for further investigation and team discussions. With contextual knowledge of the violation, the executed process or the metric itself can be refined. The metrics reflect our experiences with running a software engineering course over the last five years. They measure aspects which students frequently have issues with and that diminish process adoption and student engagement. We present the proposed metrics, which were tested in the latest course installment, alongside tutoring, lectures, and oral exams.

Beat your mom at solitaire — A review of reverse engineering techniques and countermeasures

As computer software systems grow ever more sophisticated, so do the mechanisms used in compromising them. Some of the most advanced cyber attacks in recent years would have required considerable preparation and research on the specific software applications and the hardware they targeted. In this paper we focus on native applications and evaluate different reverse engineering techniques with a focus on memory manipulation, used to compromise their security. Additionally we discuss different protection mechanism and their practicalities. The techniques discussed are executed against a well known application (i.e. the Microsoft Windows Solitaire game) and the results are presented.

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.

Senska – Towards an Enterprise Streaming Benchmark

In the light of growing data volumes and continuing digitization in fields such as Industry 4.0 or Internet of Things, data stream processing have gained popularity and importance. Especially enterprises can benefit from this development by augmenting their vital, core business data with up-to-date streaming information. Enriching this transactional data with detailed information from high-frequency data streams allows answering new analytical questions as well as improving current analyses, e.g., regarding predictive maintenance. Comparing such data stream processing architectures for use in an enterprise context, i.e., when combining streaming and business data, is currently a challenging task as there is no suitable benchmark.

Lightweight collection and storage of software repository data with DataRover

The ease of setting up collaboration infrastructures for software engineering projects creates a challenge for researchers that aim to analyze the resulting data. As teams can choose from various available software-as-a-service solutions and can configure them with a few clicks, researchers have to create and maintain multiple implementations for collecting and aggregating the collaboration data in order to perform their analyses across different setups. The DataRover system presented in this paper simplifies this task by only requiring custom source code for API authentication and querying. Data transformation and linkage is performed based on mappings, which users can define based on sample responses through a graphical front end. This allows storing the same input data in formats and databases most suitable for the intended analysis without requiring additional coding. Furthermore, API responses are continuously monitored to detect changes and allow users to update their mappings and data collectors accordingly. A screencast of the described use cases is available at https: //youtu.be/mt4ztff4SfU.