25,388 research outputs found
Mining developer communication data streams
This paper explores the concepts of modelling a software development project
as a process that results in the creation of a continuous stream of data. In
terms of the Jazz repository used in this research, one aspect of that stream
of data would be developer communication. Such data can be used to create an
evolving social network characterized by a range of metrics. This paper
presents the application of data stream mining techniques to identify the most
useful metrics for predicting build outcomes. Results are presented from
applying the Hoeffding Tree classification method used in conjunction with the
Adaptive Sliding Window (ADWIN) method for detecting concept drift. The results
indicate that only a small number of the available metrics considered have any
significance for predicting the outcome of a build
Software Metrics in Boa Large-Scale Software Mining Infrastructure: Challenges and Solutions
In this paper, we describe our experience implementing some of classic
software engineering metrics using Boa - a large-scale software repository
mining platform - and its dedicated language. We also aim to take an advantage
of the Boa infrastructure to propose new software metrics and to characterize
open source projects by software metrics to provide reference values of
software metrics based on large number of open source projects. Presented
software metrics, well known and proposed in this paper, can be used to build
large-scale software defect prediction models. Additionally, we present the
obstacles we met while developing metrics, and our analysis can be used to
improve Boa in its future releases. The implemented metrics can also be used as
a foundation for more complex explorations of open source projects and serve as
a guide how to implement software metrics using Boa as the source code of the
metrics is freely available to support reproducible research.Comment: Chapter 8 of the book "Software Engineering: Improving Practice
through Research" (B. Hnatkowska and M. \'Smia{\l}ek, eds.), pp. 131-146,
201
Data-Driven Application Maintenance: Views from the Trenches
In this paper we present our experience during design, development, and pilot
deployments of a data-driven machine learning based application maintenance
solution. We implemented a proof of concept to address a spectrum of
interrelated problems encountered in application maintenance projects including
duplicate incident ticket identification, assignee recommendation, theme
mining, and mapping of incidents to business processes. In the context of IT
services, these problems are frequently encountered, yet there is a gap in
bringing automation and optimization. Despite long-standing research around
mining and analysis of software repositories, such research outputs are not
adopted well in practice due to the constraints these solutions impose on the
users. We discuss need for designing pragmatic solutions with low barriers to
adoption and addressing right level of complexity of problems with respect to
underlying business constraints and nature of data.Comment: Earlier version of paper appearing in proceedings of the 4th
International Workshop on Software Engineering Research and Industrial
Practice (SER&IP), IEEE Press, pp. 48-54, 201
Overcoming Language Dichotomies: Toward Effective Program Comprehension for Mobile App Development
Mobile devices and platforms have become an established target for modern
software developers due to performant hardware and a large and growing user
base numbering in the billions. Despite their popularity, the software
development process for mobile apps comes with a set of unique, domain-specific
challenges rooted in program comprehension. Many of these challenges stem from
developer difficulties in reasoning about different representations of a
program, a phenomenon we define as a "language dichotomy". In this paper, we
reflect upon the various language dichotomies that contribute to open problems
in program comprehension and development for mobile apps. Furthermore, to help
guide the research community towards effective solutions for these problems, we
provide a roadmap of directions for future work.Comment: Invited Keynote Paper for the 26th IEEE/ACM International Conference
on Program Comprehension (ICPC'18
- …