Incremental program analysis (IPA) has gained increasing attention as an effective approach for maintaining up-to-date analysis results by leveraging previously computed results in response to program changes. Consequently, a variety of IPA algorithms and tools have been proposed. However, their empirical performance in practical, real-world scenarios remains insufficiently investigated. To address this gap, this study presents a comprehensive examination of the current state-of-the-art in IPA evaluation. Specifically, we identify two key limitations: (1) the lack of standardized benchmarks reflecting real-world program changes, and (2) the inadequacy and imbalanced distribution of evaluation metrics.To overcome these challenges, we propose an automated pipeline for constructing real-world program change benchmarks and develop a unified incremental evaluation framework for systematically evaluating IPA tools. Using the proposed evaluation pipeline, we constructed large-scale benchmarks of real-world program changes—sourced from 4,084 commits across 20 Java projects—and systematically evaluated two IPA tools for Java. The results demonstrate that, although incremental analysis substantially improves efficiency compared to exhaustive analysis, existing IPA tools exhibit inconsistencies and markedly higher peak memory consumption. Finally, we distill practical insights from our findings to inform future research and development in the field of incremental program analysis.

The persistent scatterer interferometric synthetic aperture radar (PSI) has proven to be a powerful tool for monitoring surface deformation. However, the conventional deformation models cannot be fully adapted to analysis of the complicated deformation process. Taking into account seasonal response and tectonic movement, this letter presents an integrated deformation model for separating deformation components through PSI time series, thus obtaining the linear deformation rate, the deformation acceleration and the seasonal deformation amplitude at each PS. An iterative solution method is proposed to estimate the parameters in the integrated model. The experiments are carried out for subsidence detection over the northwestern part of Tianjin (China) by using the 40 high-resolution TerraSAR-X SAR images acquired between 2009 and 2010 and the ground truth data collected by precise leveling at seven benchmarks and six man-made corner reflectors. The testing results indicate that the integrated model has better adaptability to the subsidence process in the study area than the conventional models. The further analysis shows that the deformation results derived from the iterative solution are in better agreement with the ground truth data. These demonstrate that the proposed methodology is effective for monitoring the surface deformation with remarkable nonlinear property.