26,098 papers · page 227 of 1,305
Expressing class specifications via executable constraints is important for various software engineering tasks such as test generation, bug finding and automated debugging, but developers rarely write them. Techniques that infer specifications from code exist to fill this gap, bu…
The notion of Attack Surface refers to the critical points on the boundary of a software system which are accessible from outside or contain valuable content for attackers. The ability to identify attack surface components of software system has a significant role in effectivenes…
The introduction of machine learning (ML) components in software projects has created the need for software engineers to collaborate with data scientists and other specialists. While collaboration can always be challenging, ML introduces additional challenges with its exploratory…
In this paper we present a methodology to analyze users' concerns and perspectives about privacy at scale. We leverage NLP techniques to process millions of mobile app reviews and extract privacy concerns. Our methodology is composed of a binary classifier that distinguishes betw…
A popular metric to evaluate the performance of fuzzers is branch coverage. However, we argue that focusing solely on covering many different branches (i.e., the richness) is not sufficient since the majority of the covered branches may have been exercised only once, which does n…
Today deep learning is widely used for building software. A software engineering problem with deep learning is that finding an appropriate convolutional neural network (CNN) model for the task can be a challenge for developers. Recent work on AutoML, more precisely neural archite…
Recent years have seen the successful application of large pre-trained models to code representation learning, resulting in substantial improvements on many code-related downstream tasks. But there are issues surrounding their application to SE tasks. First, the majority of the p…
Automated program repair is an emerging technology that seeks to automatically rectify bugs and vulnerabilities using learning, search, and semantic analysis. Trust in automatically generated patches is necessary for achieving greater adoption of program repair. Towards this goal…
In principle, continuous integration (CI) practices allow modern software organizations to build and test their products after each code change to detect quality issues as soon as possible. In reality, issues with the build scripts (e.g., missing dependencies) and/or the presence…
Training from scratch is the most common way to build a Convolutional Neural Network (CNN) based model. What if we can build new CNN models by reusing parts from previously built CNN models? What if we can improve a CNN model by replacing (possibly faulty) parts with other parts?…
Variable names are important to understand and maintain code. If a variable name and the value stored in the variable do not match, then the program suffers from a name-value inconsistency, which is due to one of two situations that developers may want to fix: Either a correct va…
Variability-aware analysis is critical for ensuring the quality of configurable C software. An important step toward the development of variability-aware analysis at scale is to transform real-world C software that uses both C and preprocessor into pure C code, by replacing the p…
Type inference for dynamic programming languages such as Python is an important yet challenging task. Static type inference techniques can precisely infer variables with enough static constraints but are unable to handle variables with dynamic features. Deep learning (DL) based a…
Software projects use build systems to automate the compilation, testing, and continuous deployment of their software products. As software becomes increasingly configurable, the build of multiple configurations is a pressing need, but expensive and challenging to implement. The …
Motivation: A key premise of open source software is the ability to copy code to other open source projects (white-box reuse). Such copying accelerates development of new projects, but the code flaws in the original projects, such as vulnerabilities, may also spread even if fixed…
Software reproducibility plays an essential role in establishing trust between source code and the built artifacts, by comparing compilation outputs acquired from independent users. Although the testing for unreproducible builds could be automated, fixing unreproducible build iss…
Modern code review is a critical and indispensable practice in a pull-request development paradigm that prevails in Open Source Software (OSS) development. Finding a suitable reviewer in projects with massive participants thus becomes an increasingly challenging task. Many review…
Software development is a collaborative process requiring a careful balance of focused individual effort and team coordination. Though questions of individual productivity have been widely examined in past literature, less is known about the interplay between developers' percepti…
We present a heuristic for approximating the likelihood of reaching a given program statement using 1) branch selectivity (representing the percentage of values that satisfy a branch condition), which we compute using model counting, 2) dependency analysis, which we use to identi…
Automatic machine learning, or AutoML, holds the promise of truly democratizing the use of machine learning (ML), by substantially automating the work of data scientists. However, the huge combinatorial search space of candidate pipelines means that current AutoML techniques, gen…