26,098 papers · page 381 of 1,305
Recurrent neural network (RNN) has achieved great success in processing sequential inputs for applications such as automatic speech recognition, natural language processing and machine translation. However, quality and reliability issues of RNNs make them vulnerable to adversaria…
Quantitative program analysis is an emerging area with applications to software reliability, quantitative information flow, side-channel detection and attack synthesis. Most quantitative program analysis techniques rely on model counting constraint solvers, which are typically th…
This paper presents InFix, a technique for automatically fixing erroneous program inputs for novice programmers. Unlike comparable existing approaches for automatic debugging and maintenance tasks, InFix repairs input data rather than source code, does not require test cases, and…
The amount of Android application is having a tremendous increasing trend, exerting pressure over practitioners and researchers around application quality, frequent releases, and quick fixing of bugs. This pressure leads practitioners to make usage of automated approaches based o…
Architecture degradation has a strong negative impact on software quality and can result in significant losses. Severe software degradation does not happen overnight. Software evolves continuously, through numerous issues, fixing bugs and adding new features, and architecture fla…
COTS software products are developed extensively on top of OSS projects, resulting in OSS reuse vulnerabilities. To detect such vulnerabilities, finding OSS reuses in COTS software has become imperative. While scalable to tens of thousands of OSS projects, existing binary-to-sour…
Previous studies showed that replying to a user review usually has a positive effect on the rating that is given by the user to the app. For example, Hassan et al. found that responding to a review increases the chances of a user updating their given rating by up to six times com…
More and more software-intensive systems employ machine learning and runtime optimization to improve their functionality by providing advanced features (e. g. personal driving assistants or recommendation engines). Such systems incorporate a number of smart software functions (SS…
Automated program repair (APR) reduces the burden of debugging by directly suggesting likely fixes for the bugs. We believe scalability, applicability, and accurate patch validation are among the main challenges for building practical APR techniques that the researchers in this a…
Automated program repair (APR) is one of the recent advances in automated software engineering aiming for reducing the burden of debugging by suggesting high-quality patches that either directly fix the bugs, or help the programmers in the course of manual debugging. We believe s…
Automated test generation and evaluation in simulation environments is a key technology for verification of automated driving (AD) applications. Search-based testing (SBT) is an approach for automated test generation that leverages optimization to efficiently generate interesting…
Field-exhaustive testing is a testing criterion suitable for object-oriented code over complex, heap-allocated, data structures. It requires test suites to contain enough test inputs to cover all feasible values for the object's fields within a certain scope (input-size bound). W…
Software defect prediction (SDP) utilizes the learning models to detect the defective modules in project, and their performance depends on the quality of training data. The previous researches mainly focus on the quality problems of class imbalance and feature redundancy. However…
We present techniques for automatically inferring formal properties of feed-forward neural networks. We observe that a significant part (if not all) of the logic of feed forward networks is captured in the activation status (on or off) of its neurons. We propose to extract patter…
Developers often want to find out how to use a certain API (e.g., FileReader.read in JDK library). API usage examples are very helpful in this regard. Over the years, many automated methods have been proposed to generate code examples by clustering and summarizing relevant code s…
Deep Learning (DL) has recently achieved tremendous success. A variety of DL frameworks and platforms play a key role to catalyze such progress. However, the differences in architecture designs and implementations of existing frameworks and platforms bring new challenges for DL s…
In popular continuous integration(CI) practice, coding is followed by building, integration and system testing, pre-release inspection, and deploying artifacts. This can reduce integration risk and speed up the development process. But large number of CI build failures may interr…
Grammar-based testing uses a given grammar to produce syntactically valid inputs. To cover program features, it is necessary to also cover input features-say, all URL variants for a URL parser. Our k-path algorithm for grammar production systematically covers syntactic elements a…
The IFDS algorithm can be compute-and memoryintensive for some large programs, often running for a long time (more than expected) or terminating prematurely after some time and/or memory budgets have been exhausted. In the latter case, the corresponding IFDS data-flow analyses ma…
Code snippets are prevalent, but are hard to reuse because they often lack an accompanying environment configuration. Most are not actively maintained, allowing for drift between the most recent possible configuration and the code snippet as the snippet becomes out-of-date over t…