activity
20232026
most citedRevisiting Neural Program Smoothing for Fuzzing

5 citations · 6 across the 7 of their papers we have counts for

collaborators

7 papers

cs.SE2026

How do Execution Features Improve Statistical Fault Localization? An Empirical Study

Marius Smytzek, Andreas Zeller

Automated fault localization helps developers find faults in large code bases. Statistical fault localization (SFL) ranks suspicious lines from pass/fail spectra, but line executio…

cs.LG2026

SecretFan: Synthesizing Realistic Data without Breaking Privacy

Laura Plein, Alexi Turcotte, Arina Hallemans +1

There is a need for synthetic training and test datasets that replicate statistical distributions of original datasets without compromising their confidentiality. A lot of research…

cs.SE2025★ 1 cited

How Execution Features Relate to Failures: An Empirical Study and Diagnosis Approach

Marius Smytzek, Martin Eberlein, Lars Grunske +1

Fault localization is a fundamental aspect of debugging, aiming to identify code regions likely responsible for failures. Traditional techniques primarily correlate statement execu…

cs.SE2024

Recommending and Release Planning of User-Driven Functionality Deletion for Mobile Apps

Maleknaz Nayebi, Konstantin Kuznetsov, Andreas Zeller +1

Evolving software with an increasing number of features poses challenges in terms of comprehensibility and usability. Traditional software release planning has predominantly focuse…

cs.SE2024

Learning Program Behavioral Models from Synthesized Input-Output Pairs

Tural Mammadov, Dietrich Klakow, Alexander Koller +1

We introduce Modelizer - a novel framework that, given a black-box program, learns a model from its input/output behavior using neural machine translation algorithms. The resulting…

cs.SE2024

AndroLog: Android Instrumentation and Code Coverage Analysis

Jordan Samhi, Andreas Zeller

Dynamic analysis has emerged as a pivotal technique for testing Android apps, enabling the detection of bugs, malicious code, and vulnerabilities. A key metric in evaluating the ef…

cs.SE2023★ 5 cited

Revisiting Neural Program Smoothing for Fuzzing

Maria-Irina Nicolae, Max Eisele, Andreas Zeller

Testing with randomly generated inputs (fuzzing) has gained significant traction due to its capacity to expose program vulnerabilities automatically. Fuzz testing campaigns generat…