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.

Virtual assistants such as Siri or Google Assistant are omnipresent.However, their development remains costly.One must either manually model the problem domain or provide thousands of labeled samples.We propose to automatically create virtual assistants based on Active Ontologies for interacting with databases.Our approach generates Active Ontologies; we use the database structure to derive a concept hierarchy and database values together with synonyms to extract information from user queries.Our approach also learns common phrases from samples, e.g. from existing Dialogflow agents.We extract pre-and postfixes and attach them to concepts, e.g. at to detect a succeeding location.The generated Active Ontologies reply to previously unseen and composed requests.The approach is not limited to virtual assistants but can be applied to any system with a textual or voice-based conversational interface such as chatbots.We evaluate our approach in three domains: tourism, hotel, and web cams.The study shows that automatically generated Active Ontologies extract relevant information from user utterances with a precision of 58%.The precision increases to 79% (recall 46%, F1 58%) when we use sample utterances.Our approach successfully transfers between domains, e.g.we learn phrases from the tourism domain and use them to reply to hotel requests without any adjustments.

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.

The emergence of natural language interfaces has led to first attempts of programming in natural language. We present ProNat, a tool for script-like programming in spoken natural language (SNL). Its agent-based architecture unifies deep natural language understanding (NLU) with modular software design. ProNat focuses on the extraction of processing flows and control structures from spoken utterances. For evaluation we have begun to build a speech corpus. First experiments are conducted in the domain of domestic robotics, but ProNat's architecture makes domain acquisition easy. Test results with spoken utterances in ProNat seem promising, but much work has to be done to achieve deep NLU.