3,705 research outputs found
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
Recommended from our members
Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL
Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N
Grand Challenges of Traceability: The Next Ten Years
In 2007, the software and systems traceability community met at the first
Natural Bridge symposium on the Grand Challenges of Traceability to establish
and address research goals for achieving effective, trustworthy, and ubiquitous
traceability. Ten years later, in 2017, the community came together to evaluate
a decade of progress towards achieving these goals. These proceedings document
some of that progress. They include a series of short position papers,
representing current work in the community organized across four process axes
of traceability practice. The sessions covered topics from Trace Strategizing,
Trace Link Creation and Evolution, Trace Link Usage, real-world applications of
Traceability, and Traceability Datasets and benchmarks. Two breakout groups
focused on the importance of creating and sharing traceability datasets within
the research community, and discussed challenges related to the adoption of
tracing techniques in industrial practice. Members of the research community
are engaged in many active, ongoing, and impactful research projects. Our hope
is that ten years from now we will be able to look back at a productive decade
of research and claim that we have achieved the overarching Grand Challenge of
Traceability, which seeks for traceability to be always present, built into the
engineering process, and for it to have "effectively disappeared without a
trace". We hope that others will see the potential that traceability has for
empowering software and systems engineers to develop higher-quality products at
increasing levels of complexity and scale, and that they will join the active
community of Software and Systems traceability researchers as we move forward
into the next decade of research
Grand Challenges of Traceability: The Next Ten Years
In 2007, the software and systems traceability community met at the first
Natural Bridge symposium on the Grand Challenges of Traceability to establish
and address research goals for achieving effective, trustworthy, and ubiquitous
traceability. Ten years later, in 2017, the community came together to evaluate
a decade of progress towards achieving these goals. These proceedings document
some of that progress. They include a series of short position papers,
representing current work in the community organized across four process axes
of traceability practice. The sessions covered topics from Trace Strategizing,
Trace Link Creation and Evolution, Trace Link Usage, real-world applications of
Traceability, and Traceability Datasets and benchmarks. Two breakout groups
focused on the importance of creating and sharing traceability datasets within
the research community, and discussed challenges related to the adoption of
tracing techniques in industrial practice. Members of the research community
are engaged in many active, ongoing, and impactful research projects. Our hope
is that ten years from now we will be able to look back at a productive decade
of research and claim that we have achieved the overarching Grand Challenge of
Traceability, which seeks for traceability to be always present, built into the
engineering process, and for it to have "effectively disappeared without a
trace". We hope that others will see the potential that traceability has for
empowering software and systems engineers to develop higher-quality products at
increasing levels of complexity and scale, and that they will join the active
community of Software and Systems traceability researchers as we move forward
into the next decade of research
FixMiner: Mining Relevant Fix Patterns for Automated Program Repair
Patching is a common activity in software development. It is generally
performed on a source code base to address bugs or add new functionalities. In
this context, given the recurrence of bugs across projects, the associated
similar patches can be leveraged to extract generic fix actions. While the
literature includes various approaches leveraging similarity among patches to
guide program repair, these approaches often do not yield fix patterns that are
tractable and reusable as actionable input to APR systems. In this paper, we
propose a systematic and automated approach to mining relevant and actionable
fix patterns based on an iterative clustering strategy applied to atomic
changes within patches. The goal of FixMiner is thus to infer separate and
reusable fix patterns that can be leveraged in other patch generation systems.
Our technique, FixMiner, leverages Rich Edit Script which is a specialized tree
structure of the edit scripts that captures the AST-level context of the code
changes. FixMiner uses different tree representations of Rich Edit Scripts for
each round of clustering to identify similar changes. These are abstract syntax
trees, edit actions trees, and code context trees. We have evaluated FixMiner
on thousands of software patches collected from open source projects.
Preliminary results show that we are able to mine accurate patterns,
efficiently exploiting change information in Rich Edit Scripts. We further
integrated the mined patterns to an automated program repair prototype,
PARFixMiner, with which we are able to correctly fix 26 bugs of the Defects4J
benchmark. Beyond this quantitative performance, we show that the mined fix
patterns are sufficiently relevant to produce patches with a high probability
of correctness: 81% of PARFixMiner's generated plausible patches are correct.Comment: 31 pages, 11 figure
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