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| Extension of existing software often
requires systematic and pervasive edits—programmers apply similar,
but not identical, enhancements, refactorings, and bug fixes to many
similar methods. The vision of this research is to produce
a novel program analysis, transformation, and
validation framework. A novel automatic edit-script
generation approach will learn abstract,
context-aware program transformations from example
edits. A novel edit-script application
algorithm automatically identifies code locations that require
similar edits, and then transforms each location with a concrete
edit that our algorithm customizes to the particular context.
Programmers may also apply edit-scripts on-demand by specifying an
edit location. Dynamic and static analysis validate edits by
testing the transformed code. This project is sponsored by National Science Foundation CAREER Award CCF-1149391: Analysis and Automation of Systematic Software Modifications. Keywords: Code Transformation; Refactoring; Static and Dynamic Analysis; Experimental Evaluation
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During collaborative
software development, developers need to analyze past and present
software modifications made by other programmers in various tasks
such as carrying out a peer code reviews, bug investigations, and
change impact analysis. CHIME project addresses the following
fundamental questions about software modifications: (1) what is a
concise and explicit representation of a program change? (2)
how do we automatically extract the differences between two program
versions into meaningful high-level representations? (3) How can
we significantly improve developer productivity in
investigating, searching, and monitoring software modifications
made by other developers? This CHIME project is sponsored by National Science Foundation Grant CCF-1117902: Analytical Support for Investigating Software Modifications in Collaborative Development Environment. Keywords: Program Differencing; Code Change Analysis; Empirical Studies; Mining Software Archives; Collaborative Software Development
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Should
we refactor code or just keep adding features? While code
decay causes multi-million dollar loss in the form of cancelled
projects or operational failures, there is not much help provided to
software practioners to answer this question. It is widely believed
that refactoring improves software quality and developer
productivity. Yet, few empirical studies quantitatively validate
refactoring benefits. This lack of empirical basis makes it
difficult for software practitioners to justify refactoring
investment or to decide when to refactor software. Our goal is to
investigate the role of refactoring during software evolution both
quantitatively and qualitatively. In particular, we propose to
quantify the cost and benefits of refactoring and its relationship
to various software metrics. We investigate the rationale,
benefits, and challenges of refactoring from developers' perspective
and investigate social and technical issues surrounding
refactorings. Keywords: Refactoring; Empirical Studies; Software Evolution; Modularity; Technical Debt
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Developing software for
cyber-physical systems is challenging because correct execution
depends not only on the logical state but also on physical
state. The fact that physical states are transient and
difficult to observe further complicates the process.
Developers must repeatedly rerun the system and continuously tweak
the hardware and software to achieve the desired behavior.
This process of manually aligning the logical and physical states of
the system is extremely labor intensive, and also lacks the rigor
and repeatability we expect of well-designed systems. BRACE is a
framework that aims to alleviate these challenges by enabling joint
assertions over both the cyber (logical) and physical properties of
the system. This framework provides a middleware assertion
library that simplifies the tedious process of examining system
states by introducing new forms of assertions, catered to the unique
demands of cyber-physical systems. Keywords: Cyber-Physicsal System; Software Correctness; Runtime Verification; Middleware |
| It has been long believed that
duplicated code fragments indicate poor software quality and
factoring out the commonality among them improves software quality;
thus, previous studies focused on measuring the percentage of code
clones and interpreted a large (or increasing) number as an
indicator for poor quality. On the other hand, we investigated how
and why duplicated code is actually created and maintained using two
empirical analyses. we used an edit capture and replay approach to
gather insights into copy and paste programming practices. To extend
this type of change-centric analysis to programs without edit logs,
we developed a clone genealogy analysis that tracks individual
clones over multiple versions. By focusing on how code clones
actually evolve, we found that clones are not inherently bad and
that we need better support for managing clones. Keywords: Code Duplication; Software Forking; Software Reuse |
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