Non-Functional Requirement Traceability Process Model for Agile Software Development

Agile methodologies have been appreciated for the fast delivery of software. They are criticized for poor handling of Non-Functional Requirements (NFRs) such as security and performance and difficulty in tracing the changes caused by updates in NFR that are also associated with Functional Requirements (FRs).This paper presents a novel approach named Traceability process model of Agile Software Development for Tracing NFR change impact (TANC). In order to validate TANC’s compatibility with most of Agile process models, we present a logical model that synchronizes TANC with the two of enhanced models: secure feature-driven development (SFDD) and secured scrum (SScrum).Then, we conducted a case study on TANC using a tool support called Sagile. In terms of adaptability with agile process model, the logical model could be depicted in SFDD and the case study proved that TANC is carried out successfully in SFDD

Semantically Enhanced Software Traceability Using Deep Learning Techniques

In most safety-critical domains the need for traceability is prescribed by certifying bodies. Trace links are generally created among requirements, design, source code, test cases and other artifacts, however, creating such links manually is time consuming and error prone. Automated solutions use information retrieval and machine learning techniques to generate trace links, however, current techniques fail to understand semantics of the software artifacts or to integrate domain knowledge into the tracing process and therefore tend to deliver imprecise and inaccurate results. In this paper, we present a solution that uses deep learning to incorporate requirements artifact semantics and domain knowledge into the tracing solution. We propose a tracing network architecture that utilizes Word Embedding and Recurrent Neural Network (RNN) models to generate trace links. Word embedding learns word vectors that represent knowledge of the domain corpus and RNN uses these word vectors to learn the sentence semantics of requirements artifacts. We trained 360 different configurations of the tracing network using existing trace links in the Positive Train Control domain and identified the Bidirectional Gated Recurrent Unit (BI-GRU) as the best model for the tracing task. BI-GRU significantly out-performed state-of-the-art tracing methods including the Vector Space Model and Latent Semantic Indexing.Comment: 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE