39,128 research outputs found
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
Performing Hybrid Recommendation in Intermodal Transportation – the FTMarket System’s Recommendation Module
Diverse recommendation techniques have been already proposed and encapsulated into several e-business applications, aiming to perform a more accurate evaluation of the existing information and accordingly augment the assistance provided to the users involved. This paper reports on the development and integration of a recommendation module in an agent-based transportation transactions management system. The module is built according to a novel hybrid recommendation technique, which combines the advantages of collaborative filtering and knowledge-based approaches. The proposed technique and supporting module assist customers in considering in detail alternative transportation transactions that satisfy their requests, as well as in evaluating completed transactions. The related services are invoked through a software agent that constructs the appropriate knowledge rules and performs a synthesis of the recommendation policy
Recursion Aware Modeling and Discovery For Hierarchical Software Event Log Analysis (Extended)
This extended paper presents 1) a novel hierarchy and recursion extension to
the process tree model; and 2) the first, recursion aware process model
discovery technique that leverages hierarchical information in event logs,
typically available for software systems. This technique allows us to analyze
the operational processes of software systems under real-life conditions at
multiple levels of granularity. The work can be positioned in-between reverse
engineering and process mining. An implementation of the proposed approach is
available as a ProM plugin. Experimental results based on real-life (software)
event logs demonstrate the feasibility and usefulness of the approach and show
the huge potential to speed up discovery by exploiting the available hierarchy.Comment: Extended version (14 pages total) of the paper Recursion Aware
Modeling and Discovery For Hierarchical Software Event Log Analysis. This
Technical Report version includes the guarantee proofs for the proposed
discovery algorithm
Data-driven design of intelligent wireless networks: an overview and tutorial
Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves
What is the Connection Between Issues, Bugs, and Enhancements? (Lessons Learned from 800+ Software Projects)
Agile teams juggle multiple tasks so professionals are often assigned to
multiple projects, especially in service organizations that monitor and
maintain a large suite of software for a large user base. If we could predict
changes in project conditions changes, then managers could better adjust the
staff allocated to those projects.This paper builds such a predictor using data
from 832 open source and proprietary applications. Using a time series analysis
of the last 4 months of issues, we can forecast how many bug reports and
enhancement requests will be generated next month. The forecasts made in this
way only require a frequency count of this issue reports (and do not require an
historical record of bugs found in the project). That is, this kind of
predictive model is very easy to deploy within a project. We hence strongly
recommend this method for forecasting future issues, enhancements, and bugs in
a project.Comment: Accepted to 2018 International Conference on Software Engineering, at
the software engineering in practice track. 10 pages, 10 figure
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