Rationale in Development Chat Messages: An Exploratory Study

Chat messages of development teams play an increasingly significant role in software development, having replaced emails in some cases. Chat messages contain information about discussed issues, considered alternatives and argumentation leading to the decisions made during software development. These elements, defined as rationale, are invaluable during software evolution for documenting and reusing development knowledge. Rationale is also essential for coping with changes and for effective maintenance of the software system. However, exploiting the rationale hidden in the chat messages is challenging due to the high volume of unstructured messages covering a wide range of topics. This work presents the results of an exploratory study examining the frequency of rationale in chat messages, the completeness of the available rationale and the potential of automatic techniques for rationale extraction. For this purpose, we apply content analysis and machine learning techniques on more than 8,700 chat messages from three software development projects. Our results show that chat messages are a rich source of rationale and that machine learning is a promising technique for detecting rationale and identifying different rationale elements.Comment: 11 pages, 6 figures. The 14th International Conference on Mining Software Repositories (MSR'17

Learning an Orchestra Conductor's Technique Using a Wearable Sensor Platform

Our study focuses on finding new input devices for a system allowing users with any skill to configure and conduct a virtual orchestra in real-time. As a first step, we conducted a user study to learn more about the interaction between a conductor's gestures and the orchestra 's reaction. During an orchestra rehearsal session, we observed a conductor's timing and gestures using the eWatch, a wrist-worn wearable computer and sensor platform. The gestures are analyzed and compared to the music of the orchestra

Starling: A Blockchain-based System for Coordinated Obstacle Mapping in Dynamic Vehicular Environments

Current Vehicle-to-Vehicle solutions cannot ensure the authenticity of safety-critical vehicle and traffic data. Moreover, they do not allow malicious vehicles to be detected and eliminated. However, this is becoming mandatory, as more and more vehicles are on the road and communicating with each other. We propose a system called Starling, which focuses on trusted coordinated obstacle mapping using blockchain technology and a distributed database. Starling enables vehicles to share detected obstacles with other vehicles in a secure and verifiable manner, thus improving road safety. It ensures that data was not manipulated, changed, or deleted and is based on an open protocol so that vehicles can exchange data regardless of their manufacturer. In a case study, we demonstrate how a consensus is reached among vehicles and conduct a comprehensive evaluation of the Starling system using Ethereum and the InterPlanetary File System

Tool Support for Decision and Usage Knowledge in Continuous Software Engineering

Continuous software engineering copes with frequent changes and quickly evolving development projects while maintaining a high software quality. Developers require knowledge about former and ongoing decisions as well as about the users' needs to evolve software. Thus, decision and usage knowledge are two important knowledge types in continuous software engineering. Issue tracking and version control systems are widely used in continuous software engineering but lack a structured approach to integrate decision and usage knowledge. In this paper, we present ideas and requirements for a tool support to manage decision and usage knowledge in continuous software engineering. As a first step, we introduce the JIRA DecDoc plug-in for documenting decision knowledge

Sharing and Exploiting Requirement Decisions

Continuous software engineering is an agile development process that puts particular emphasis on the incremental implementation of requirements and their rapid validation through user feedback. This involves frequent and incremental decision making, which needs to be shared within the team. Requirements engineers and developers have to share their decision knowledge since the decisions made are particularly important for future requirements. It has been a vision for long that important decision knowledge gets documented and shared. However, several reasons hinder requirements engineers and developers from doing this, for example, the intrusiveness and overhead of the documentation. Current software development tools provide opportunities to minimize the overhead. Issue tracking and version control systems offer lightweight documentation locations, such as issue comments, commit messages, and code comments. With ConDec, we develop tool support for the continuous management of decision knowledge that uses techniques for natural language processing and integrates into tools that developers often use, for example, into the issue tracking system Jira. In this work, we focus on how ConDec enables requirements engineers and developers to share and exploit decision knowledge regarding requirements

Continuous Management of Requirement Decisions Using the ConDec Tools

Context and motivation: While eliciting, prioritizing, and implementing requirements, requirements engineers and developers continuously make decisions. They establish important decision knowledge that needs to be documented and exploited, i.e., thoroughly managed, so that it contributes to the evolution and future changes of a software system. Question/problem: The management of decision knowledge is difficult for various reasons: 1) The documentation process is an additional effort, i.e., it is intrusive in the development process. 2) The documented knowledge can be of low quality in terms of completeness and consistency. 3) It might be distributed across many documentation locations, such as issue comments and commit messages, and thus difficult to access and use. Principal ideas/results: Continuous software engineering (CSE) emerged as a development process that involves frequent, incremental decision making, implementation, and validation of requirements. During CSE, requirements engineers and developers implicitly document decision knowledge during established practices, such as committing code, working with issues in an issue tracking system, or conducting meetings. That means that CSE offers opportunities for the non-intrusive capturing of decision knowledgein various documentation locations. Contribution: We develop the ConDec tools (https://se.ifi.uni-heidelberg.de/condec.html) that support requirements engineers and developers in documenting and exploiting decision knowledge directly within the tools they use, such as issue tracking and wiki systems, related to various software artifacts, such as requirements and code, and during change impact analysis

Towards a Systematic Approach to Integrate Usage and Decision Knowledge in Continuous Software Engineering

Abstract: Continuous software engineering (CSE) employs activities such as continuous integration and continuous delivery to support software evolution. Another aspect of software evolution is knowledge management. There are two important knowledge types: usage knowledge derives from explicit and implicit user feedback and helps to understand how users utilize software. Decision knowledge encompasses decisions and their rationale on all aspects of the software lifecycle. Both knowledge types represent important information sources for developers to improve the CSE activities and the software product. We envision an integration of usage and decision knowledge in the CSE lifecycle. This extension consists of a monitoring and feedback component for user understanding as well as a knowledge repository and dashboard component for knowledge visualization and analysis. Usage and decision knowledge introduce challenges when integrating them in CSE. In this paper, we present our vision and detail the challenges

Teaching Rationale Management in Agile Project Courses

Rationale management is beneficial since it supports decision-making and prevents knowledge vaporization. To apply rationale management, developers need to know how to systematically capture rationale and how to exploit the documentation. We believe that teaching these skills to students further integrates rationale management into the daily work of developers and has positive effects on both the software development process and on the quality of the software. In this paper, we report on a lecture on teaching rationale management to students. In this lecture, students are introduced to a rationale model, to capture and exploitation methods, and tool support for rationale management. The goal is to motivate them to apply rationale management. We present the students' results as well as their attitude and feedback towards the applied methods. Further, we sketch how rationale management will be applied during the semester

The relentless variability of Mrk 421 from the TeV to the radio

The origin of the gamma-ray emission of the blazar Mrk 421 is still a matter of debate. We used 5.5 years of unbiased observing campaign data, obtained using the FACT telescope and the Fermi LAT detector at TeV and GeV energies, the longest and densest so far, together with contemporaneous multi-wavelength observations, to characterise the variability of Mrk 421 and to constrain the underlying physical mechanisms. We studied and correlated light curves obtained by ten different instruments and found two significant results. The TeV and X-ray light curves are very well correlated with a lag of <0.6 days. The GeV and radio (15 Ghz band) light curves are widely and strongly correlated. Variations of the GeV light curve lead those in the radio. Lepto-hadronic and purely hadronic models in the frame of shock acceleration predict proton acceleration or cooling timescales that are ruled out by the short variability timescales and delays observed in Mrk 421. Instead the observations match the predictions of leptonic models.Comment: 10 pages, 8 figures, 1 tabl