Generating and visualizing trace link explanations
Generating and visualizing trace link explanations
复制标题
生成并可视化跟踪链接解释
DOI：
10.1145/3510003.3510129
复制
发表时间：
2022
期刊：
影响因子：
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通讯作者：
Cleland-Huang, Jane
中科院分区：
文献类型：
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作者：
Liu, Yalin;Lin, Jinfeng;Anuyah, Oghenemaro;Metoyer, Ronald;Cleland-Huang, Jane
深度学习（DL）方法的最新突破导致了跟踪链接的动态生成，其准确性远远超过以前。然而，DL生成的链接缺乏明确的解释，因此该领域的非专家很难理解链接的底层语义，这使得他们很难评估链接的正确性或适用于特定的软件工程任务。在本文中，我们提出了一种新的NLP管道生成和可视化跟踪链接的解释。我们的方法确定特定领域的概念，检索语料库的概念相关的句子，挖掘概念的定义和使用的例子，并确定跨工件概念之间的关系，以解释的链接。它采用了一个后处理步骤，以确定最可能的缩略语和定义的优先次序，并删除不相关的缩略语和定义。我们使用来自星际望远镜、正向列车控制和电子医疗系统三个不同领域的项目工件来评估我们的方法，然后报告所生成定义的覆盖范围、正确性和潜在效用。我们设计和利用一个解释接口，利用概念定义和关系，可视化和解释跟踪链接的理由，我们报告的结果，进行用户研究，以评估解释界面的有效性。结果表明，在接口中提供的解释，帮助非专家理解跟踪链接的底层语义，提高他们的能力，以审查的正确性链接。
Recent breakthroughs in deep-learning (DL) approaches have resulted in the dynamic generation of trace links that are far more accurate than was previously possible. However, DL-generated links lack clear explanations, and therefore non-experts in the domain can find it difficult to understand the underlying semantics of the link, making it hard for them to evaluate the link's correctness or suitability for a specific software engineering task. In this paper we present a novel NLP pipeline for generating and visualizing trace link explanations. Our approach identifies domain-specific concepts, retrieves a corpus of concept-related sentences, mines concept definitions and usage examples, and identifies relations between cross-artifact concepts in order to explain the links. It applies a post-processing step to prioritize the most likely acronyms and definitions and to eliminate non-relevant ones. We evaluate our approach using project artifacts from three different domains of interstellar telescopes, positive train control, and electronic healthcare systems, and then report coverage, correctness, and potential utility of the generated definitions. We design and utilize an explanation interface which leverages concept definitions and relations to visualize and explain trace link rationales, and we report results from a user study that was conducted to evaluate the effectiveness of the explanation interface. Results show that the explanations presented in the interface helped non-experts to understand the underlying semantics of a trace link and improved their ability to vet the correctness of the link.