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.
Safety Assurance Cases (SACs) are increasingly used to guide and evaluate the safety of software-intensive systems. They are used to construct a hierarchically organized set of claims, arguments, and ...evidence in order to provide a structured argument that a system is safe for use. However, as the system evolves and grows in size, a SAC can be difficult to maintain. In this paper we utilize design science to develop a novel solution for identifying areas of a SAC that are affected by changes to the system. Moreover, we generate actionable recommendations for updating the SAC, including its underlying artifacts and trace links, in order to evolve an existing safety case for use in a new version of the system. Our approach, Safety Artifact Forest Analysis (SAFA), leverages traceability to automatically compare software artifacts from a previously approved or certified version with a new version of the system. We identify, visualize, and explain changes in a Delta Tree. We evaluate our approach using the Dronology system for monitoring and coordinating the actions of cooperating, small Unmanned Aerial Vehicles. Results from a user study show that SAFA helped users to identify changes that potentially impacted system safety and provided information that could be used to help maintain and evolve a SAC.
Requirements engineering in large‐scale industrial, government, and international projects can be a highly complex process involving thousands or even hundreds of thousands of potentially distributed ...stakeholders. The process can result in massive amounts of noisy and semistructured data that must be analyzed and distilled in order to extract useful requirements. As a result, many human‐intensive tasks in requirements elicitation, analysis, and management processes can be augmented and supported through the use of recommender system and machine‐learning techniques. In this article we describe several areas in which recommendation technologies have been applied to the requirements engineering domain, namely stakeholder identification, domain analysis, requirements elicitation, and decision support across several requirements analysis and prioritization tasks. We also highlight ongoing challenges and opportunities for applying recommender systems in the requirements engineering domain.
Software architectures are often constructed through a series of design decisions. In particular, architectural tactics are selected to satisfy specific quality concerns such as reliability, ...performance, and security. However, the knowledge of these tactical decisions is often lost, resulting in a gradual degradation of architectural quality as developers modify the code without fully understanding the underlying architectural decisions. In this paper we present a machine learning approach for discovering and visualizing architectural tactics in code, mapping these code segments to tactic traceability patterns, and monitoring sensitive areas of the code for modification events in order to provide users with up-to-date information about underlying architectural concerns. Our approach utilizes a customized classifier which is trained using code extracted from fifty performance-centric and safety-critical open source software systems. Its performance is compared against seven off-the-shelf classifiers. In a controlled experiment all classifiers performed well; however our tactic detector outperformed the other classifiers when used within the larger context of the Hadoop Distributed File System. We further demonstrate the viability of our approach for using the automatically detected tactics to generate viable and informative messages in a simulation of maintenance events mined from Hadoop's change management system.
Safety stories specify safety requirements, using the EARS (Easy Requirements Specification) format. Software practitioners can use them in agile projects at lower levels of safety criticality to ...deal effectively with safety concerns.
Many guidelines for safety-critical industries such as aeronautics, medical devices, and railway communications, specify that traceability must be used to demonstrate that a rigorous process has been ...followed and to provide evidence that the system is safe for use. In practice, there is a gap between what is prescribed by guidelines and what is implemented in practice, making it difficult for organizations and certifiers to fully evaluate the safety of the software system. In this paper we present an approach, which parses a guideline to extract a Traceability Model depicting software artifact types and their prescribed traces. It then analyzes the traceability data within a project to identify areas of traceability failure. Missing traceability paths, redundant and/or inconsistent data, and other problems are highlighted. We used our approach to evaluate the traceability of seven safety-critical software systems and found that none of the evaluated projects contained traceability that fully conformed to its relevant guidelines.
Basic data-mining skills can be useful for processing domain documents early during requirements engineering. An example from the electronic-healthcare-records domain shows how. The Web extra at ...http://youtu.be/tHvi3pHEP8c is an audio podcast in which author Jane Cleland-Huang provides an audio recording of the Requirements column, in which she discusses how basic data-mining skills can be useful for processing domain documents early during requirements engineering.
Traceability provides support for diverse software engineering activities including safety analysis, compliance verification, test-case selection, and impact prediction. However, in practice, there ...is a tendency for trace links to degrade over time as the system continually evolves. This is especially true for links between source-code and upstream artifacts such as requirements – because developers frequently refactor and change code without updating the links. In this paper we present TLE (Trace Link Evolver), a solution for automating the evolution of bidirectional trace links between source code classes or methods and requirements. TLE depends on a set of heuristics coupled with refactoring detection tools and informational retrieval algorithms to detect predefined change scenarios that occur across contiguous versions of a software system. We first evaluate TLE at the class level in a controlled experiment to evolve trace links for revisions of two Java applications. Second, we comparatively evaluate several variants of TLE across six releases of our in-house Dronology project. We study the results of integrating human analyst feed back in the evolution cycle of this emerging project. Additionally, in this system, we compare the efficacy of class-level versus method-level evolution of trace links. Finally, we evaluate TLE in a larger scale across 27 releases of the Cassandra Database System and show that the evolved trace links are significantly more accurate than those generated using only information retrieval techniques.
Software traceability links are established between diverse artifacts of the software development process in order to support tasks such as compliance analysis, safety assurance, and requirements ...validation. However, practice has shown that it is difficult and costly to create and maintain trace links in non-trivially sized projects. For this reason, many researchers have proposed and evaluated automated approaches based on information retrieval and deep-learning. Generating trace links automatically can also be challenging – especially in multi-national projects which include artifacts written in multiple languages. The intermingled language use can reduce the efficiency of automated tracing solutions. In this work, we analyze patterns of intermingled language that we observed in several different projects, and then comparatively evaluate different tracing algorithms. These include Information Retrieval techniques, such as the Vector Space Model (VSM), Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA), and various models that combine mono- and cross-lingual word embeddings with the Generative Vector Space Model (GVSM), and a deep-learning approach based on a BERT language model. Our experimental analysis of trace links generated for 14 Chinese-English projects indicates that our MultiLingual Trace-BERT approach performed best in large projects with close to 2-times the accuracy of the best IR approach, while the IR-based GVSM with neural machine translation and a monolingual word embedding performed best on small projects.
Stakeholders on the Prowl Cleland-Huang, Jane
IEEE software,
2016-Mar.-Apr., 2016-3-00, 20160301, Letnik:
33, Številka:
2
Journal Article
Recenzirano
Working with diverse stakeholders is a fact of life for any requirements engineer. And learning to bring out the best in each of them is an art acquired over time. This article shares effective ...stakeholder interaction techniques for solving three common problems. The Web extra at https://youtu.be/1df3HmRTbBk is an audio podcast in which author Jane Cleland-Huang provides an audio recording of the Requirements column.
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