There are a multitude of software artifacts which need to be handled during the development and maintenance of a software system. These artifacts interrelate in multiple, complex ways. Therefore, many software engineering tasks are enabled - and even empowered - by a clear understanding of artifact interrelationships and also by the continued advancement of techniques for automated artifact linking. However, current approaches in automatic Traceability Link Recovery (TLR) target mostly the links between specific sets of artifacts, such as those between requirements and code. Fortu-nately, recent advancements in Large Language Models (LLMs) can enable TLR approaches to achieve broad applicability. Still, it is a nontrivial problem how to provide the LLMs with the specific information needed to perform TLR. In this paper, we present LiSSA, a framework that har-nesses LLM performance and enhances them through Retrieval-Augmented Generation (RAG). We empirically evaluate LiSSA on three different TLR tasks, requirements to code, documentation to code, and architecture documentation to architecture models, and we compare our approach to state-of-the-art approaches. Our results show that the RAG-based approach can signifi-cantly outperform the state-of-the-art on the code-related tasks. However, further research is required to improve the performance of RAG-based approaches to be applicable in practice.

Introduction Software development involves creating various artifacts at different levels of abstraction and establishing relationships between them is essential. Traceability link recovery (TLR) automates this process, enhancing software quality by aiding tasks like maintenance and evolution. However, automating TLR is challenging due to semantic gaps resulting from different levels of abstraction. While automated TLR approaches exist for requirements and code, architecture documentation lacks tailored solutions, hindering the preservation of architecture knowledge and design decisions. Methods This paper presents our approach TransArC for TLR between architecture documentation and code, using component-based architecture models as intermediate artifacts to bridge the semantic gap. We create transitive trace links by combining the existing approach ArDoCo for linking architecture documentation to models with our novel approach ArCoTL for linking architecture models to code.

Being aware of and understanding the relations between the requirements of a software system to its other artifacts is crucial for their successful development, maintenance and evolution. There are approaches to automatically recover this traceability information, but they fail to identify the actual relevant parts of the requirements. Recent large language model-based requirements classification approaches have shown to be able to identify aspects and concerns of requirements with promising accuracy. Therefore, we investigate the potential of those classification approaches for identifying irrelevant requirement parts for traceability link recovery between requirements and code. We train the large language model-based requirements classification approach NoRBERT on a new dataset of requirements and their entailed aspects and concerns. We use the results of the classification to filter irrelevant parts of the requirements before recovering trace links with the fine-grained word embedding-based FTLR approach. Two empirical studies show promising results regarding the quality of classification and the impact on traceability link recov-ery. NoRBERT can identify functional and user-related aspects in the requirements with an F I-score of 84 %. With the classification and requirements filtering, the performance of FTLR could be improved significantly and FTLR performs better than state-of-the-art unsupervised traceability link recovery approaches.

Traceability information is a fundamental prerequisite for many essential software maintenance and evolution tasks, such as change impact and software reusability analyses. However, manually generating traceability information is costly and error-prone. Therefore, researchers have developed automated approaches that utilize textual similarities between artifacts to establish trace links. These approaches tend to achieve low precision at reasonable recall levels, as they are not able to bridge the semantic gap between high-level natural language requirements and code. We propose to overcome this limitation by leveraging fine-grained, method and sentence level, similarities between the artifacts for traceability link recovery. Our approach uses word embeddings and a Word Mover's Distance-based similarity to bridge the semantic gap. The fine-grained similarities are aggregated according to the artifacts structure and participate in a majority vote to retrieve coarse-grained, requirement-to-class, trace links. In a comprehensive empirical evaluation, we show that our approach is able to outperform state-of-the-art unsupervised traceability link recovery approaches. Additionally, we illustrate the benefits of fine-grained structural analyses to word embedding-based trace link generation.

The key to effortless end-user programming is natural language.We examine how to teach intelligent systems new functions, expressed in natural language.As a first step, we collected 3168 samples of teaching efforts in plain English.Then we built fu SE , a novel system that translates English function descriptions into code.Our approach is three-tiered and each task is evaluated separately.We first classify whether an intent to teach new functionality is present in the utterance (accuracy: 97.7% using BERT).Then we analyze the linguistic structure and construct a semantic model (accuracy: 97.6% using a BiLSTM).Finally, we synthesize the signature of the method, map the intermediate steps (instructions in the method body) to API calls and inject control structures (F 1 : 67.0% with information retrieval and knowledge-based methods).In an end-to-end evaluation on an unseen dataset fu SE synthesized 84.6% of the method signatures and 79.2% of the API calls correctly.

Classifying requirements is crucial for automatically handling natural language requirements. The performance of existing automatic classification approaches diminishes when applied to unseen projects because requirements usually vary in wording and style. The main problem is poor generalization. We propose NoRBERT that fine-tunes BERT, a language model that has proven useful for transfer learning. We apply our approach to different tasks in the domain of requirements classification. We achieve similar or better results F1-scores of up to 94%) on both seen and unseen projects for classifying functional and non-functional requirements on the PROMISE NFR dataset. NoRBERT outperforms recent approaches at classifying non-functional requirements subclasses. The most frequent classes are classified with an average F1-score of 87%. In an unseen project setup on a relabeled PROMISE NFR dataset, our approach achieves an improvement of 15 percentage points in average F1score compared to recent approaches. Additionally, we propose to classify functional requirements according to the included concerns, i.e., function, data, and behavior. We labeled the functional requirements in the PROMISE NFR dataset and applied our approach. NoRBERT achieves an F1-score of up to 92%. Overall, NoRBERT improves requirements classification and can be applied to unseen projects with convincing results.

Current systems with spoken language interfaces do not leverage contextual information.Therefore, they struggle with understanding speakers' intentions.We propose a system that creates a context model from user utterances to overcome this lack of information.It comprises eight types of contextual information organized in three layers: individual, conceptual, and hierarchical.We have implemented our approach as a part of the project PARSE.It aims at enabling laypersons to construct simple programs by dialog.Our implementation incrementally generates context including occurring entities and actions as well as their conceptualizations, state transitions, and other types of contextual information.Its analyses are knowledge-or rulebased (depending on the context type), but we make use of many well-known probabilistic NLP techniques.In a user study we have shown the feasibility of our approach, achieving F1 scores from 72% up to 98% depending on the type of contextual information.The context model enables us to resolve complex identity relations.However, quantifying this effect is subject to future work.Likewise, we plan to investigate whether our context model is useful for other language understanding tasks, e.g., anaphora resolution, topic analysis, or correction of automatic speech recognition errors.

Current systems with spoken language interfaces do not leverage contextual information. Therefore, they struggle with understanding speakers’ intentions. We propose a system that creates a context model from user utterances to overcome this lack of information. It comprises eight types of contextual information organized in three layers: individual, conceptual, and hierarchical. We have implemented our approach as a part of the project PARSE. It aims at enabling laypersons to construct simple programs by dialog. Our implementation incrementally generates context including occurring entities and actions as well as their conceptualizations, state transitions, and other types of contextual information. Its analyses are knowledge- or rule-based (depending on the context type), but we make use of many well-known probabilistic NLP techniques. In a user study we have shown the feasibility of our approach, achieving [Formula: see text] scores from 72% up to 98% depending on the type of contextual information. The context model enables us to resolve complex identity relations. However, quantifying this effect is subject to future work. Likewise, we plan to investigate whether our context model is useful for other language understanding tasks, e.g. anaphora resolution, topic analysis, or correction of automatic speech recognition errors.