Feature Reuse Across Software Releases During Software Evolution1

Software evolution relies on storing component versions along with delta-changes in a repository of a version control tool such a centralized CVS in old days, or decentralized Git today. Code implementing various software features (e.g., requirements) often spreads over multiple software components, and across multiple versions of those components. Not having a clear picture of feature implementation and evolution may hinder software reuse which most often is concerned with feature reuse across system releases, and components are just means to that end. Much research on feature location shows how important and difficult is to find feature-related code buried in program components post mortem. We propose to avoid creating the problem in the first place, by explicating feature-related code in component versions at the time of their implementation. To do that, we complement traditional version control approach with generative mechanisms. We describe salient features of such an approach realized in ART (Adaptive Reuse Technology, http://art-processor.org), and explain its role in easing comprehending software evolution and feature reuse. Advanced commercial version control tools make a step towards easing the evolution problems addressed in this paper. Our approach is an alternative way of addressing the same problem on quite a different ground.

Aspect-Level Sentiment Analysis Approach via BERT and Aspect Feature Location Model

With the rapid development of Internet social platforms, buyer shows (such as comment text) have become an important basis for consumers to understand products and purchase decisions. The early sentiment analysis methods were mainly text-level and sentence-level, which believed that a text had only one sentiment. This phenomenon will cover up the details, and it is difficult to reflect people’s fine-grained and comprehensive sentiments fully, leading to people’s wrong decisions. Obviously, aspect-level sentiment analysis can obtain a more comprehensive sentiment classification by mining the sentiment tendencies of different aspects in the comment text. However, the existing aspect-level sentiment analysis methods mainly focus on attention mechanism and recurrent neural network. They lack emotional sensitivity to the position of aspect words and tend to ignore long-term dependencies. In order to solve this problem, on the basis of Bidirectional Encoder Representations from Transformers (BERT), this paper proposes an effective aspect-level sentiment analysis approach (ALM-BERT) by constructing an aspect feature location model. Specifically, we use the pretrained BERT model first to mine more aspect-level auxiliary information from the comment context. Secondly, for the sake of learning the expression features of aspect words and the interactive information of aspect words’ context, we construct an aspect-based sentiment feature extraction method. Finally, we construct evaluation experiments on three benchmark datasets. The experimental results show that the aspect-level sentiment analysis performance of the ALM-BERT approach proposed in this paper is significantly better than other comparison methods.

Gravitational effects of scene information in object localization

We effortlessly interact with objects in our environment, but how do we know where something is? An object’s apparent position does not simply correspond to its retinotopic location but is influenced by its surrounding context. In the natural environment, this context is highly complex, and little is known about how visual information in a scene influences the apparent location of the objects within it. We measured the influence of local image statistics (luminance, edges, object boundaries, and saliency) on the reported location of a brief target superimposed on images of natural scenes. For each image statistic, we calculated the difference between the image value at the physical center of the target and the value at its reported center, using observers’ cursor responses, and averaged the resulting values across all trials. To isolate image-specific effects, difference scores were compared to a randomly-permuted null distribution that accounted for any response biases. The observed difference scores indicated that responses were significantly biased toward darker regions, luminance edges, object boundaries, and areas of high saliency, with relatively low shared variance among these measures. In addition, we show that the same image statistics were associated with observers’ saccade errors, despite large differences in response time, and that some effects persisted when high-level scene processing was disrupted by 180° rotations and color negatives of the originals. Together, these results provide evidence for landmark effects within natural images, in which feature location reports are pulled toward low- and high-level informative content in the scene.