Behind the Intents: An In-depth Empirical Study on Software Refactoring in Modern Code Review

Code refactorings are of pivotal importance in modern code review. Developers may preserve, revisit, add or undo refactorings through changes’ revisions. Their goal is to certify that the driving intent of a code change is properly achieved. Developers’ intents behind refactorings may vary from pure structural improvement to facilitating feature additions and bug fixes. However, there is little understanding of the refactoring practices performed by developers during the code review process. It is also unclear whether the developers’ intents influence the selection, composition, and evolution of refactorings during the review of a code change. Through mining 1,780 reviewed code changes from 6 systems pertaining to two large open-source communities, we report the first in-depth empirical study on software refactoring during code review. We inspected and classified the developers’ intents behind each code change into 7 distinct categories. By analyzing data generated during the complete reviewing process, we observe: (i) how refactorings are selected, composed and evolved throughout each code change, and (ii) how developers’ intents are related to these decisions. For instance, our analysis shows developers regularly apply non-trivial sequences of refactorings that crosscut multiple code elements (i.e., widely scattered in the program) to support a single feature addition. Moreover, we observed that new developers’ intents commonly emerge during the code review process, influencing how developers select and compose their refactorings to achieve the new and adapted goals. Finally, we provide an enriched dataset that allows researchers to investigate the context and motivations behind refactoring operations during the code review process

Quantification of Imatinib Plasma Levels in Patients with Chronic Myeloid Leukemia: Comparison Between HPLCUV and LCMS/MS

Measurement of imatinib plasma concentration can be useful to evaluate patient adherence to daily oral therapy, potential drug–drug interaction, treatment efficacy, and severe drug-related adverse events. The aim of this study was to correlate the two different blood level test methods, HPLC–UV and LC–MS/MS. We analyzed 162 plasma samples from patients treated with imatinib. We estimated the correlation between the two analytical methods on total data set and on five sets of patients grouped into different categories based on the drug-dose therapy. Finally, imatinib quantification was correlated with genetic data on the molecular response in monitoring follow-up of CML patients