Constructing a static call graph requires trade-offs between soundness and
precision. Program analysis techniques for constructing call graphs are
unfortunately usually imprecise. To address this problem, researchers have
recently proposed call graph pruning empowered by machine learning to
post-process call graphs constructed by static analysis. A machine learning
model is built to capture information from the call graph by extracting
structural features for use in a random forest classifier. It then removes
edges that are predicted to be false positives. Despite the improvements shown
by machine learning models, they are still limited as they do not consider the
source code semantics and thus often are not able to effectively distinguish
true and false positives. In this paper, we present a novel call graph pruning
technique, AutoPruner, for eliminating false positives in call graphs via both
statistical semantic and structural analysis. Given a call graph constructed by
traditional static analysis tools, AutoPruner takes a Transformer-based
approach to capture the semantic relationships between the caller and callee
functions associated with each edge in the call graph. To do so, AutoPruner
fine-tunes a model of code that was pre-trained on a large corpus to represent
source code based on descriptions of its semantics. Next, the model is used to
extract semantic features from the functions related to each edge in the call
graph. AutoPruner uses these semantic features together with the structural
features extracted from the call graph to classify each edge via a feed-forward
neural network. Our empirical evaluation on a benchmark dataset of real-world
programs shows that AutoPruner outperforms the state-of-the-art baselines,
improving on F-measure by up to 13% in identifying false-positive edges in a
static call graph.

本文提出了一种名为 AutoPruner 的新型基于 Transformer 的调用图剪枝技术，通过统计语义和结构分析来消除调用图中的 false positive，相比现有技术在识别静态调用图中的 false positive 方面提高了 13% 的 F-measure 表现。

AutoPruner：基于 Transformer 的调用图剪枝

AutoPruner: Transformer-Based Call Graph Pruning

Channel pruning is an important family of methods to speed up deep model's
inference. Previous filter pruning algorithms regard channel pruning and model
fine-tuning as two independent steps. This paper argues that combining them
into a single end-to-end trainable system will lead to better results. We
propose an efficient channel selection layer, namely AutoPruner, to find less
important filters automatically in a joint training manner. Our AutoPruner
takes previous activation responses as an input and generates a true binary
index code for pruning. Hence, all the filters corresponding to zero index
values can be removed safely after training. We empirically demonstrate that
the gradient information of this channel selection layer is also helpful for
the whole model training. By gradually erasing several weak filters, we can
prevent an excessive drop in model accuracy. Compared with previous
state-of-the-art pruning algorithms (including training from scratch),
AutoPruner achieves significantly better performance. Furthermore, ablation
experiments show that the proposed novel mini-batch pooling and binarization
operations are vital for the success of filter pruning.

通过将通道修剪和模型微调合并为单个端到端可训练的系统，使用自动修剪器来找到不重要的滤波器。所生成的真二元指数代码可以安全地删除运用于微调后的模型中。相较于之前的优秀修剪算法，AutoPruner 能够显著提高性能。慢慢擦除几个弱滤波器可以防止模型精度的过度下降。