With the accelerated development of software, developers face the continuous challenge of fixing vulnerabilities but vulnerability-fixing commits often disassociated from the vulnerabilities, and the structural and semantic differences between code changes and natural language present significant challenges in identifying these commits. Existing approaches utilize machine learning and deep learning techniques to address this problem, but they often do not fully leverage the information about code changes. In this paper, we propose VFProber, a method based on a code change pretrained model, aiming to provide a comprehensive and unified framework for identifying vulnerability-fixing commits. VFProber uses semantic adjustment to distinguish between context-sensitive and context-insensitive code units in code changes, thereby enhancing the model’s understanding of code changes during the training process. Secondly, VFProber employs a novel code change pretrained model as a feature extractor. Compared with ordinary code pretrained models, it can better meet the requirements of the vulnerability-fixing identification task. Moreover, we constructed a vulnerability-fixing dataset containing two common programming languages, Java and JavaScript, from industrial projects. In the experimental section, we designed three tasks to evaluate the method. The results show that, compared with the best baseline, VFProber performs better in the vulnerability-fixing identification task and can effectively reduce false positives and false negatives.

To promptly identify software defects and prevent defective code changes from being integrated into the repository, Just-In-Time Software Defect Prediction (JIT-SDP) has demonstrated promising research findings. Recent studies have begun to utilize Pre-trained Models (PTMs) for training and prediction, yet these models inherently impose input length limitations, leading to forced truncation of inputs. However, previous work has largely overlooked the impact of forced truncation, even though it may inadvertently discard critical input information, leading to degraded model performance. Moreover, some existing methods fail to maintain consistency in truncation during each model construction process, leading to unexplainable truncations and unstable model performance. In addition, previous datasets suffer from limitations and incompleteness. To this end, we construct a large-scale and comprehensive dataset, MC4Defect. Moreover, we propose JIT-Align, which prioritizes code changes within a commit using a semantic alignment algorithm to make full use of the limited input space of PTMs. To evaluate the feasibility of JIT-Align, we first assess the classification capability of our method by comparing it against four baselines across five datasets. Then, we conduct ablation studies on the proposed semantic alignment framework to validate its effectiveness. Experimental results show that JIT-Align, along with its semantic alignment framework, outperforms all baselines in JIT-SDP tasks, with average F1 score improvements of 3.1%-9.6% and MCC increases of 3.1%-9.7% across all projects, exhibiting higher stability and better interpretability compared to alternative approaches.

Multistep cell fate transitions with stepwise changes of transcriptional profiles are common to many developmental, regenerative and pathological processes. The multiple intermediate cell lineage states can serve as differentiation checkpoints or branching points for channeling cells to more than one lineages. However, mechanisms underlying these transitions remain elusive. Here, we explored gene regulatory circuits that can generate multiple intermediate cellular states with stepwise modulations of transcription factors. With unbiased searching in the network topology space, we found a motif family containing a large set of networks can give rise to four attractors with the stepwise regulations of transcription factors, which limit the reversibility of three consecutive steps of the lineage transition. We found that there is an enrichment of these motifs in a transcriptional network controlling the early T cell development, and a mathematical model based on this network recapitulates multistep transitions in the early T cell lineage commitment. By calculating the energy landscape and minimum action paths for the T cell model, we quantified the stochastic dynamics of the critical factors in response to the differentiation signal with fluctuations. These results are in good agreement with experimental observations and they suggest the stable characteristics of the intermediate states in the T cell differentiation. These dynamical features may help to direct the cells to correct lineages during development. Our findings provide general design principles for multistep cell linage transitions and new insights into the early T cell development. The network motifs containing a large family of topologies can be useful for analyzing diverse biological systems with multistep transitions.