Test Report Generation for Android App Testing Via Heterogeneous Data Analysis
The rising of the Android market demands higher quality assurance of Android applications (apps) to sharpen the competitive edge, and techniques for traditional software have problems adapting for mobile apps. Android apps often require testing on a large-...
Challenging Machine Learning-Based Clone Detectors via Semantic-Preserving Code Transformations
Software clone detection identifies similar or identical code snippets. It has been an active research topic that attracts extensive attention over the last two decades. In recent years, machine learning (ML) based detectors, especially deep learning-...
TrinityRCL: Multi-Granular and Code-Level Root Cause Localization Using Multiple Types of Telemetry Data in Microservice Systems
The microservice architecture has been commonly adopted by large scale software systems exemplified by a wide range of online services. Service monitoring through anomaly detection and root cause analysis (RCA) is crucial for these microservice systems to ...
Static Analysis of JNI Programs via Binary Decompilation
JNI programs are widely used thanks to the combined benefits of C and Java programs. However, because understanding the interaction behaviors between two different programming languages is challenging, JNI program development is difficult to get right and ...
Understanding Mentors’ Engagement in OSS Communities via Google Summer of Code
A constant influx of newcomers is essential for the sustainability and success of open source software (OSS) projects. However, successful onboarding is always challenging because newcomers face various initial contributing barriers. To support newcomer ...
Trace Diagnostics for Signal-Based Temporal Properties
Trace checking is a verification technique widely used in Cyber-physical system (CPS) development, to verify whether execution traces satisfy or violate properties expressing system requirements. Often these properties characterize complex signal ...
<sc>Plumber</sc>: Boosting the Propagation of Vulnerability Fixes in the <italic>npm</italic> Ecosystem
Vulnerabilities are known reported security threats that affect a large amount of packages in the <italic>npm</italic> ecosystem. To mitigate these security threats, the open-source community strongly suggests vulnerable packages to timely publish ...
Black-Box Testing of Deep Neural Networks through Test Case Diversity
Deep Neural Networks (DNNs) have been extensively used in many areas including image processing, medical diagnostics and autonomous driving. However, DNNs can exhibit erroneous behaviours that may lead to critical errors, especially when used in safety-...
NCQ: Code Reuse Support for Node.js Developers
Code reuse is an important part of software development. The adoption of code reuse practices is especially common among Node.js developers. The Node.js package manager, NPM, indexes over 1 Million packages and developers often seek out packages to solve ...
Beyond Literal Meaning: Uncover and Explain Implicit Knowledge in Code Through Wikipedia-Based Concept Linking
When reusing or modifying code, developers need to understand the implicit knowledge behind a piece of code in addition to the literal meaning of code. Such implicit knowledge involves related concepts and their explanations. Uncovering and understanding ...
Empirical Validation of Automated Vulnerability Curation and Characterization
- Ahmet Okutan,
- Peter Mell,
- Mehdi Mirakhorli,
- Igor Khokhlov,
- Joanna C. S. Santos,
- Danielle Gonzalez,
- Steven Simmons
Prior research has shown that public vulnerability systems such as US National Vulnerability Database (NVD) rely on a manual, time-consuming, and error-prone process which has led to inconsistencies and delays in releasing final vulnerability results. ...
Modelling Second-Order Uncertainty in State Machines
Modelling the behaviour of state-based systems can be challenging, especially when the modeller is not entirely certain about its intended interactions with the user or the environment. Currently, it is possible to associate a stated level of uncertainty ...
Verification of Fuzzy Decision Trees
In recent years, there have been major strides in the safety verification of machine learning models such as neural networks and tree ensembles. However, fuzzy decision trees (FDT), also called soft or differentiable decision trees, are yet unstudied in ...
API Usage Recommendation Via Multi-View Heterogeneous Graph Representation Learning
Developers often need to decide which APIs to use for the functions being implemented. With the ever-growing number of APIs and libraries, it becomes increasingly difficult for developers to find appropriate APIs, indicating the necessity of automatic API ...
Automatically Tagging the “AAA” Pattern in Unit Test Cases Using Machine Learning Models
The <italic>AAA</italic> pattern (i.e., <italic>Arrange-Act-Assert</italic>) is a common and natural layout to create a test case. Following this pattern in test cases may benefit comprehension, debugging, and maintenance. The <italic>AAA</italic> ...
A Framework for Emotion-Oriented Requirements Change Handling in Agile Software Engineering
<bold>Background:</bold> Requirements Changes (RCs) – the additions/modifications/deletions of functional/non-functional requirements in software products – are challenging for software practitioners to handle. Handling some changes may ...
