11,364 research outputs found
Mining Fix Patterns for FindBugs Violations
In this paper, we first collect and track a large number of fixed and unfixed
violations across revisions of software.
The empirical analyses reveal that there are discrepancies in the
distributions of violations that are detected and those that are fixed, in
terms of occurrences, spread and categories, which can provide insights into
prioritizing violations.
To automatically identify patterns in violations and their fixes, we propose
an approach that utilizes convolutional neural networks to learn features and
clustering to regroup similar instances. We then evaluate the usefulness of the
identified fix patterns by applying them to unfixed violations.
The results show that developers will accept and merge a majority (69/116) of
fixes generated from the inferred fix patterns. It is also noteworthy that the
yielded patterns are applicable to four real bugs in the Defects4J major
benchmark for software testing and automated repair.Comment: Accepted for IEEE Transactions on Software Engineerin
Learning Tractable Probabilistic Models for Fault Localization
In recent years, several probabilistic techniques have been applied to
various debugging problems. However, most existing probabilistic debugging
systems use relatively simple statistical models, and fail to generalize across
multiple programs. In this work, we propose Tractable Fault Localization Models
(TFLMs) that can be learned from data, and probabilistically infer the location
of the bug. While most previous statistical debugging methods generalize over
many executions of a single program, TFLMs are trained on a corpus of
previously seen buggy programs, and learn to identify recurring patterns of
bugs. Widely-used fault localization techniques such as TARANTULA evaluate the
suspiciousness of each line in isolation; in contrast, a TFLM defines a joint
probability distribution over buggy indicator variables for each line. Joint
distributions with rich dependency structure are often computationally
intractable; TFLMs avoid this by exploiting recent developments in tractable
probabilistic models (specifically, Relational SPNs). Further, TFLMs can
incorporate additional sources of information, including coverage-based
features such as TARANTULA. We evaluate the fault localization performance of
TFLMs that include TARANTULA scores as features in the probabilistic model. Our
study shows that the learned TFLMs isolate bugs more effectively than previous
statistical methods or using TARANTULA directly.Comment: Fifth International Workshop on Statistical Relational AI (StaR-AI
2015
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
A Top-Down Approach to Managing Variability in Robotics Algorithms
One of the defining features of the field of robotics is its breadth and
heterogeneity. Unfortunately, despite the availability of several robotics
middleware services, robotics software still fails to smoothly handle at least
two kinds of variability: algorithmic variability and lower-level variability.
The consequence is that implementations of algorithms are hard to understand
and impacted by changes to lower-level details such as the choice or
configuration of sensors or actuators. Moreover, when several algorithms or
algorithmic variants are available it is difficult to compare and combine them.
In order to alleviate these problems we propose a top-down approach to
express and implement robotics algorithms and families of algorithms so that
they are both less dependent on lower-level details and easier to understand
and combine. This approach goes top-down from the algorithms and shields them
from lower-level details by introducing very high level abstractions atop the
intermediate abstractions of robotics middleware. This approach is illustrated
on 7 variants of the Bug family that were implemented using both laser and
infra-red sensors.Comment: 6 pages, 5 figures, Presented at DSLRob 2013 (arXiv:cs/1312.5952
Modeling and Testing Implementations of Protocols with Complex Messages
This paper presents a new language called APSL for formally describing
protocols to facilitate automated testing. Many real world communication
protocols exchange messages whose structures are not trivial, e.g. they may
consist of multiple and nested fields, some could be optional, and some may
have values that depend on other fields. To properly test implementations of
such a protocol, it is not sufficient to only explore different orders of
sending and receiving messages. We also need to investigate if the
implementation indeed produces correctly formatted messages, and if it responds
correctly when it receives different variations of every message type. APSL's
main contribution is its sublanguage that is expressive enough to describe
complex message formats, both text-based and binary. As an example, this paper
also presents a case study where APSL is used to model and test a subset of
Courier IMAP email server
Statistical Assertions for Validating Patterns and Finding Bugs in Quantum Programs
In support of the growing interest in quantum computing experimentation,
programmers need new tools to write quantum algorithms as program code.
Compared to debugging classical programs, debugging quantum programs is
difficult because programmers have limited ability to probe the internal states
of quantum programs; those states are difficult to interpret even when
observations exist; and programmers do not yet have guidelines for what to
check for when building quantum programs. In this work, we present quantum
program assertions based on statistical tests on classical observations. These
allow programmers to decide if a quantum program state matches its expected
value in one of classical, superposition, or entangled types of states. We
extend an existing quantum programming language with the ability to specify
quantum assertions, which our tool then checks in a quantum program simulator.
We use these assertions to debug three benchmark quantum programs in factoring,
search, and chemistry. We share what types of bugs are possible, and lay out a
strategy for using quantum programming patterns to place assertions and prevent
bugs.Comment: In The 46th Annual International Symposium on Computer Architecture
(ISCA '19). arXiv admin note: text overlap with arXiv:1811.0544
A Concurrent Perspective on Smart Contracts
In this paper, we explore remarkable similarities between multi-transactional
behaviors of smart contracts in cryptocurrencies such as Ethereum and classical
problems of shared-memory concurrency. We examine two real-world examples from
the Ethereum blockchain and analyzing how they are vulnerable to bugs that are
closely reminiscent to those that often occur in traditional concurrent
programs. We then elaborate on the relation between observable contract
behaviors and well-studied concurrency topics, such as atomicity, interference,
synchronization, and resource ownership. The described
contracts-as-concurrent-objects analogy provides deeper understanding of
potential threats for smart contracts, indicate better engineering practices,
and enable applications of existing state-of-the-art formal verification
techniques.Comment: 15 page
- …