Generating meaningful assert statements is one of the key challenges in automated test case generation, which requires understanding the intended functionality of the tested code. Recently, deep learning based models have shown promise in improving the performance of assert statement generation. However, the existing models only rely on the test prefixes along with their corresponding focal methods, yet ignore the developer-written summarization. Based on our observations, the summarization contents usually express the intended program behavior or contain parameters that will appear directly in the assert statement. Such information will help existing models address their current inability to accurately predict assert statements. This paper presents a summarization-guided approach for automatically generating assert statements. To derive generic representations for natural language (i.e., summarization) and programming language (i.e., test prefixes and focal methods), we leverage a pre-trained language model as the reference architecture and fine-tune it on the task of assert statement generation. To the best of our knowledge, the proposed approach makes the first attempt to leverage the summarization of focal methods as the guidance for making the generated assert statements more accurate. We demonstrate the effectiveness of our approach on two real-world datasets compared with state-of-the-art models.

Many applications need to meet diverse requirements of a large-scale distributed user group. That challenges the current requirements engineering techniques. Crowd-based requirements engineering was proposed as an umbrella term for dealing with the requirements development in the context of the large-scale user group. However, there are still many issues. Among others, a key issue is how to merge these requirements to produce the synthesized requirements description when a set of requirements descriptions from different participants are received. Appropriate techniques are needed for supporting the requirements synthesis. Diagrams are widely used in industry to represent requirements. This paper chooses the activity diagrams and proposes a novel approach for the activity diagram synthesis which adopts the genetic algorithm to repeatedly modify a population of individual solutions toward an optimal solution. As a result, it can automatically generate a resulting diagram which combines the commonalities as many as possible while leveraging the variabilities of a set of input diagrams. The approach is featured by: 1) the labelled graph proposed as the representation of the candidate solutions during the iterative evolution; 2) the generalized entropy proposed and defined as the measurement of the solutions; 3) the genetic algorithm designed for sorting out the high-quality solution. Four cases of different scales are used to evaluate the effectiveness of the approach. The experimental results show that not only the approach gets high precision and recall but also the resulting diagram satisfies the properties of minimization and information preservation and can support the requirements traceability.