Change-centric improvement of team collaboration

In software development, teamwork is essential to the successful delivery of a final product. The software industry has historically built software utilizing development teams that share the workplace. Process models, tools, and methodologies have been enhanced to support the development of software in a collocated setting. However, since the dawn of the 21st century, this scenario has begun to change: an increasing number of software companies are adopting global software development to cut costs and speed up the development process. Global software development introduces several challenges for the creation of quality software, from the adaptation of current methods, tools, techniques, etc., to new challenges imposed by the distributed setting, including physical and cultural distance between teams, communication problems, and coordination breakdowns. A particular challenge for distributed teams is the maintenance of a level of collaboration naturally present in collocated teams. Collaboration in this situation naturally d r ops due to low awareness of the activity of the team. Awareness is intrinsic to a collocated team, being obtained through human interaction such as informal conversation or meetings. For a distributed team, however, geographical distance and a subsequent lack of human interaction negatively impact this awareness. This dissertation focuses on the improvement of collaboration, especially within geographically dispersed teams. Our thesis is that by modeling the evolution of a software system in terms of fine-grained changes, we can produce a detailed history that may be leveraged to help developers collaborate. To validate this claim, we first c r eate a model to accurately represent the evolution of a system as sequences of fine- grained changes. We proceed to build a tool infrastructure able to capture and store fine-grained changes for both immediate and later use. Upon this foundation, we devise and evaluate a number of applications for our work with two distinct goals: 1. To assist developers with real-time information about the activity of the team. These applications aim to improve developers’ awareness of team member activity that can impact their work. We propose visualizations to notify developers of ongoing change activity, as well as a new technique for detecting and informing developers about potential emerging conflicts. 2. To help developers satisfy their needs for information related to the evolution of the software system. These applications aim to exploit the detailed change history generated by our approach in order to help developers find answers to questions arising during their work. To this end, we present two new measurements of code expertise, and a novel approach to replaying past changes according to user-defined criteria. We evaluate the approach and applications by adopting appropriate empirical methods for each case. A total of two case studies – one controlled experiment, and one qualitative user study – are reported. The results provide evidence that applications leveraging a fine-grained change history of a software system can effectively help developers collaborate in a distributed setting

Investigating Trade-offs For Fair Machine Learning Systems

Fairness in software systems aims to provide algorithms that operate in a nondiscriminatory manner, with respect to protected attributes such as gender, race, or age. Ensuring fairness is a crucial non-functional property of data-driven Machine Learning systems. Several approaches (i.e., bias mitigation methods) have been proposed in the literature to reduce bias of Machine Learning systems. However, this often comes hand in hand with performance deterioration. Therefore, this thesis addresses trade-offs that practitioners face when debiasing Machine Learning systems. At first, we perform a literature review to investigate the current state of the art for debiasing Machine Learning systems. This includes an overview of existing debiasing techniques and how they are evaluated (e.g., how is bias measured). As a second contribution, we propose a benchmarking approach that allows for an evaluation and comparison of bias mitigation methods and their trade-offs (i.e., how much performance is sacrificed for improving fairness). Afterwards, we propose a debiasing method ourselves, which modifies already trained Machine Learning models, with the goal to improve both, their fairness and accuracy. Moreover, this thesis addresses the challenge of how to deal with fairness with regards to age. This question is answered with an empirical evaluation on real-world datasets

Meta-Data of Mining Software Repositories (MSR) conference Papers from 2004 to 2016

<p>The <b>Mining Software Repositories (MSR) conference</b> is an annual conference started as a one-day workshop in the year 2004 (co-located with International Conference on Software Engineering ICSE 2004 at Edinburgh, UK) with the aim of brining software analytics researchers and practitioners from both university and industry around the world to exchange research results and ideas. MSR 2004 (Edinburgh, UK) was the first edition of the event and the annual event completes 13 editions in 2016 (Texas, USA). The dataset contains meta-data about 13 years of MSR papers. A paper titled "Thirteen Years of Mining Software Repositories (MSR) Conference - What is the Bibliography Data Telling Us?" by Lov Kumar and Ashish Sureka is published on Arxiv. The link the paper is <a href="https://arxiv.org/abs/1609.06266">https://arxiv.org/abs/1609.06266</a> </p

Source code authorship attribution

To attribute authorship means to identify the true author among many candidates for samples of work of unknown or contentious authorship. Authorship attribution is a prolific research area for natural language, but much less so for source code, with eight other research groups having published empirical results concerning the accuracy of their approaches to date. Authorship attribution of source code is the focus of this thesis. We first review, reimplement, and benchmark all existing published methods to establish a consistent set of accuracy scores. This is done using four newly constructed and significant source code collections comprising samples from academic sources, freelance sources, and multiple programming languages. The collections developed are the most comprehensive to date in the field. We then propose a novel information retrieval method for source code authorship attribution. In this method, source code features from the collection samples are tokenised, converted into n-grams, and indexed for stylistic comparison to query samples using the Okapi BM25 similarity measure. Authorship of the top ranked sample is used to classify authorship of each query, and the proportion of times that this is correct determines overall accuracy. The results show that this approach is more accurate than the best approach from the previous work for three of the four collections. The accuracy of the new method is then explored in the context of author style evolving over time, by experimenting with a collection of student programming assignments that spans three semesters with established relative timestamps. We find that it takes one full semester for individual coding styles to stabilise, which is essential knowledge for ongoing authorship attribution studies and quality control in general. We conclude the research by extending both the new information retrieval method and previous methods to provide a complete set of benchmarks for advancing the field. In the final evaluation, we show that the n-gram approaches are leading the field, with accuracy scores for some collections around 90% for a one-in-ten classification problem

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Design and Application of Quantified Self Approaches for Reflective Learning in the Workplace

Learning by reflection is one of the core processes for improving work performance. We provide a novel approach for reflective learning support by transferring and adapting practices from the Quantified Self to workplace settings. This book contributes with an integrated model for technical support of reflective learning, mobile and web-based applications designed for quantifying and gathering data in the workplace, and empirical insights from thirteen studies in three different use cases