Related papers: Towards Automated Performance Bug Identification i…
Self-adaptive software can assess and modify its behavior when the assessment indicates that the program is not performing as intended or when improved functionality or performance is available. Since the mid-1960s, the subject of system…
Performative prediction is an emerging paradigm in machine learning that addresses scenarios where the model's prediction may induce a shift in the distribution of the data it aims to predict. Current works in this field often rely on…
Predicting the number of defects in a project is critical for project test managers to allocate budget, resources, and schedule for testing, support and maintenance efforts. Software Defect Prediction models predict the number of defects in…
While significant progress has been made in automating various aspects of software development through coding agents, there is still significant room for improvement in their bug fixing capabilities. Debugging and investigation of runtime…
Accurate prediction of application performance is critical for enabling effective scheduling and resource management in resource-constrained dynamic edge environments. However, achieving predictable performance in such environments remains…
Regression bugs refer to situations in which something that worked previously no longer works currently. Such bugs have been pronounced in the Linux kernel. The paper focuses on regression bug tracking in the kernel by considering the time…
When predictions are performative, the choice of which predictor to deploy influences the distribution of future observations. The overarching goal in learning under performativity is to find a predictor that has low \emph{performative…
Defect prediction models---classifiers that identify defect-prone software modules---have configurable parameters that control their characteristics (e.g., the number of trees in a random forest). Recent studies show that these classifiers…
Background: Developers spend a significant amount of time and efforts to localize bugs. In the literature, many researchers proposed state-of-the-art bug localization models to help developers localize bugs easily. The practitioners, on the…
Performance analysis has always been an afterthought during the application development process, focusing on application correctness first. The learning curve of the existing static and dynamic analysis tools are steep, which requires…
Instant payment infrastructures have stringent performance requirements, processing millions of transactions daily with zero-downtime expectations. Traditional monitoring approaches fail to bridge the gap between technical infrastructure…
Predictive models are often introduced to decision-making tasks under the rationale that they improve performance over an existing decision-making policy. However, it is challenging to compare predictive performance against an existing…
The rapid pace of large-scale software development places increasing demands on traditional testing methodologies, often leading to bottlenecks in efficiency, accuracy, and coverage. We propose a novel perspective on software testing by…
Recent studies have shown that bugs can be categorized into intrinsic and extrinsic types. Intrinsic bugs can be backtracked to specific changes in the version control system (VCS), while extrinsic bugs originate from external changes to…
One of the primary mechanisms by which developers receive feedback about in-field failures of software from users is through bug reports. Unfortunately, the quality of manually written bug reports can vary widely due to the effort required…
Python is a widely adopted programming language, valued for its simplicity and flexibility. However, its dynamic type system poses significant challenges for automated refactoring - an essential practice in software evolution aimed at…
Cloud performance fluctuates due to factors such as resource contention and workload changes. These factors can be short-term, seasonal, or long-term. Their effects are often intertwined in performance traces, making performance management…
The continuous evolution of software projects necessitates the implementation of changes to enhance performance and reduce defects. This research explores effective strategies for learning and implementing useful changes in software…
Existing code benchmarks measure whether an agent can produce any test that reproduces a known bug, or whether it can produce a patch that fixes a described issue. Neither isolates the distinct skill of property-based testing: deriving a…
Cyber-physical systems (CPS) integrate sensing, computing, communication and actuation capabilities to monitor and control operations in the physical environment. A key requirement of such systems is the need to provide predictable…