20 citations · 101 across the 31 of their papers we have counts for
19 papers · 1 filter
Speed at the Cost of Quality: How Cursor AI Increases Short-Term Velocity and Long-Term Complexity in Open-Source Projects
Hao He, Courtney Miller, Shyam Agarwal +2
Large language models (LLMs) have demonstrated the promise to revolutionize the field of software engineering. Among other things, LLM agents are rapidly gaining momentum in softwa…
Beyond the Comfort Zone: Emerging Solutions to Overcome Challenges in Integrating LLMs into Software Products
Nadia Nahar, Christian Kästner, Jenna Butler +3
Large Language Models (LLMs) are increasingly embedded into software products across diverse industries, enhancing user experiences, but at the same time introducing numerous chall…
A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitioners
Nadia Nahar, Haoran Zhang, Grace Lewis +2
Incorporating machine learning (ML) components into software products raises new software-engineering challenges and exacerbates existing challenges. Many researchers have invested…
MLTEing Models: Negotiating, Evaluating, and Documenting Model and System Qualities
Katherine R. Maffey, Kyle Dotterrer, Jennifer Niemann +3
Many organizations seek to ensure that machine learning (ML) and artificial intelligence (AI) systems work as intended in production but currently do not have a cohesive methodolog…
Data Leakage in Notebooks: Static Detection and Better Processes
Chenyang Yang, Rachel A Brower-Sinning, Grace A. Lewis +1
Data science pipelines to train and evaluate models with machine learning may contain bugs just like any other code. Leakage between training and test data can lead to overestimati…
Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process
Nadia Nahar, Shurui Zhou, Grace Lewis +1
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. Whi…
Feature Interactions on Steroids: On the Composition of ML Models
Christian Kästner, Eunsuk Kang, Sven Apel
The lack of specifications is a key difference between traditional software engineering and machine learning. We discuss how it drastically impacts how we think about divide-and-co…
Efficiently Finding Higher-Order Mutants
Chu-Pan Wong, Jens Meinicke, Leo Chen +3
Higher-order mutation has the potential for improving major drawbacks of traditional first-order mutation, such as by simulating more realistic faults or improving test optimizatio…
Teaching Software Engineering for AI-Enabled Systems
Christian Kästner, Eunsuk Kang
Software engineers have significant expertise to offer when building intelligent systems, drawing on decades of experience and methods for building systems that are scalable, respo…
How Do Code Changes Evolve in Different Platforms? A Mining-based Investigation
Markos Viggiato, Johnatan Oliveira, Eduardo Figueiredo +2
Code changes are performed differently in the mobile and non-mobile platforms. Prior work has investigated the differences in specific platforms. However, we still lack a deeper un…
Design Dimensions for Software Certification: A Grounded Analysis
Gabriel Ferreira, Christian Kästner, Joshua Sunshine +2
In many domains, software systems cannot be deployed until authorities judge them fit for use in an intended operating environment. Certification standards and processes have been…
ConfigCrusher: Towards White-Box Performance Analysis for Configurable Systems
Miguel Velez, Pooyan Jamshidi, Florian Sattler +3
Stakeholders of configurable systems are often interested in knowing how configuration options influence the performance of a system to facilitate, for example, the debugging and o…
Understanding Differences among Executions with Variational Traces
Jens Meinicke, Chu-Pan Wong, Christian Kästner +1
One of the main challenges of debugging is to understand why the program fails for certain inputs but succeeds for others. This becomes especially difficult if the fault is caused…
On the Relation of External and Internal Feature Interactions: A Case Study
Sergiy Kolesnikov, Norbert Siegmund, Christian Kästner +1
Detecting feature interactions is imperative for accurately predicting performance of highly-configurable systems. State-of-the-art performance prediction techniques rely on superv…
Differential Testing for Variational Analyses: Experience from Developing KConfigReader
Christian Kästner
Differential testing to solve the oracle problem has been applied in many scenarios where multiple supposedly equivalent implementations exist, such as multiple implementations of…
Transfer Learning for Improving Model Predictions in Highly Configurable Software
Pooyan Jamshidi, Miguel Velez, Christian Kästner +2
Modern software systems are built to be used in dynamic environments using configuration capabilities to adapt to changes and external uncertainties. In a self-adaptation context,…
Do #ifdefs Influence the Occurrence of Vulnerabilities? An Empirical Study of the Linux Kernel
Gabriel Ferreira, Momin Malik, Christian Kästner +2
Preprocessors support the diversification of software products with #ifdefs, but also require additional effort from developers to maintain and understand variable code. We conject…
A Comparison of 10 Sampling Algorithms for Configurable Systems
Flávio Medeiros, Christian Kästner, Márcio Ribeiro +2
Almost every software system provides configuration options to tailor the system to the target platform and application scenario. Often, this configurability renders the analysis o…
Type-Safe Feature-Oriented Product Lines
Sven Apel, Christian Kaestner, Armin Groesslinger +1
A feature-oriented product line is a family of programs that share a common set of features. A feature implements a stakeholder's requirement, represents a design decision and conf…