What have we learnt from the challenges of (semi-) automated requirements traceability? A discussion on blockchain applicability.

Over the last 3 decades, researchers have attempted to shed light into the requirements traceability problem by introducing tracing tools, techniques, and methods with the vision of achieving ubiquitous traceability. Despite the technological advances, requirements traceability remains problematic for researchers and practitioners. This study aims to identify and investigate the main challenges in implementing (semi-)automated requirements traceability, as reported in the recent literature. A systematic literature review was carried out based on the guidelines for systematic literature reviews in software engineering, proposed by Kitchenham. We retrieved 4530 studies by searching five major bibliographic databases and selected 70 primary studies. These studies were analysed and classified according to the challenges they present and/or address. Twenty-one challenges were identified and were classified into five categories. Findings reveal that the most frequent challenges are technological challenges, in particular, low accuracy of traceability recovery methods. Findings also suggest that future research efforts should be devoted to the human facet of tracing, to explore traceability practices in organisational settings, and to develop traceability approaches that support agile and DevOps practices. Finally, it is recommended that researchers leverage blockchain technology as a suitable technical solution to ensure the trustworthiness of traceability information in interorganisational software projects.publishedVersio

Recovering from a Decade: A Systematic Mapping of Information Retrieval Approaches to Software Traceability

Engineers in large-scale software development have to manage large amounts of information, spread across many artifacts. Several researchers have proposed expressing retrieval of trace links among artifacts, i.e. trace recovery, as an Information Retrieval (IR) problem. The objective of this study is to produce a map of work on IR-based trace recovery, with a particular focus on previous evaluations and strength of evidence. We conducted a systematic mapping of IR-based trace recovery. Of the 79 publications classified, a majority applied algebraic IR models. While a set of studies on students indicate that IR-based trace recovery tools support certain work tasks, most previous studies do not go beyond reporting precision and recall of candidate trace links from evaluations using datasets containing less than 500 artifacts. Our review identified a need of industrial case studies. Furthermore, we conclude that the overall quality of reporting should be improved regarding both context and tool details, measures reported, and use of IR terminology. Finally, based on our empirical findings, we present suggestions on how to advance research on IR-based trace recovery

Information Retrieval based requirement traceability recovery approaches- A systematic literature review

Abstract: The term traceability is an important concept regarding software development. It enables software engineers to trace requirements from their origin to fulfillment. Maintaining traceability manually is a time consuming and expensive job. Information retrieval methods provide a mean of automation for requirement traceability. A visible number of IR based traceability techniques have been proposed in the literature, but the adoption of these techniques in the industry is limited. In this paper, we examine the information retrieval-based traceability recovery approaches through systematic literature review. We presented a synthesis of these techniques. We also identified challenges that are potentially limiting the adoption of IR based traceability recovery approaches. We conclude that term mismatch is a major barrier faced by IR based approaches. We also did classify the approaches that are attempting to solve the term mismatch problem

Toward an Effective Automated Tracing Process

Traceability is defined as the ability to establish, record, and maintain dependency relations among various software artifacts in a software system, in both a forwards and backwards direction, throughout the multiple phases of the project’s life cycle. The availability of traceability information has been proven vital to several software engineering activities such as program comprehension, impact analysis, feature location, software reuse, and verification and validation (V&V). The research on automated software traceability has noticeably advanced in the past few years. Various methodologies and tools have been proposed in the literature to provide automatic support for establishing and maintaining traceability information in software systems. This movement is motivated by the increasing attention traceability has been receiving as a critical element of any rigorous software development process. However, despite these major advances, traceability implementation and use is still not pervasive in industry. In particular, traceability tools are still far from achieving performance levels that are adequate for practical applications. Such low levels of accuracy require software engineers working with traceability tools to spend a considerable amount of their time verifying the generated traceability information, a process that is often described as tedious, exhaustive, and error-prone. Motivated by these observations, and building upon a growing body of work in this area, in this dissertation we explore several research directions related to enhancing the performance of automated tracing tools and techniques. In particular, our work addresses several issues related to the various aspects of the IR-based automated tracing process, including trace link retrieval, performance enhancement, and the role of the human in the process. Our main objective is to achieve performance levels, in terms of accuracy, efficiency, and usability, that are adequate for practical applications, and ultimately to accomplish a successful technology transfer from research to industry

Datasets Used in Fifteen Years of Automated Requirements Traceability Research

Datasets are crucial to advance automated software traceability research. Acquiring such datasets come in a high cost and require expert knowledge to manually collect and validate them. Obtaining such software development datasets has been one of the most frequently reported barrier for researchers in the software engineering domain in general. This problem is even more acute in field of requirement traceability, which plays crucial role in safety critical and highly regulated systems. Therefore, the main motivation behind this work is to analyze the current state of art of datasets used in the field of software traceability. This work presents a first-of-its-kind literature study to review and assess the datasets that have been used in software traceability research over the last fifteen years. It articulates several attributes related to these datasets such as their characteristics, threats and diversity. Firstly, 202 primary studies (refer Appendix A) were identified for purpose of this study, which were used to derive 73 unique datasets. These 73 datasets were studied in-depth and several attributes (size, type, domain, availability, artifacts) were extracted (refer Appendix B). Based on analysis of the primary studies, a threat to validity reference model, tailored to Software traceability datasets was derived (refer to figure 4.4). Furthermore, to put some light upon the dataset diversity trend in the Software traceability community, a metric called Dataset Diversity Ratio was derived for 38 authors (refer to figure 4.5) who have published more than one publication in field of software traceability

From Bugs to Decision Support – Leveraging Historical Issue Reports in Software Evolution

Software developers in large projects work in complex information landscapes and staying on top of all relevant software artifacts is an acknowledged challenge. As software systems often evolve over many years, a large number of issue reports is typically managed during the lifetime of a system, representing the units of work needed for its improvement, e.g., defects to fix, requested features, or missing documentation. Efficient management of incoming issue reports requires the successful navigation of the information landscape of a project. In this thesis, we address two tasks involved in issue management: Issue Assignment (IA) and Change Impact Analysis (CIA). IA is the early task of allocating an issue report to a development team, and CIA is the subsequent activity of identifying how source code changes affect the existing software artifacts. While IA is fundamental in all large software projects, CIA is particularly important to safety-critical development. Our solution approach, grounded on surveys of industry practice as well as scientific literature, is to support navigation by combining information retrieval and machine learning into Recommendation Systems for Software Engineering (RSSE). While the sheer number of incoming issue reports might challenge the overview of a human developer, our techniques instead benefit from the availability of ever-growing training data. We leverage the volume of issue reports to develop accurate decision support for software evolution. We evaluate our proposals both by deploying an RSSE in two development teams, and by simulation scenarios, i.e., we assess the correctness of the RSSEs' output when replaying the historical inflow of issue reports. In total, more than 60,000 historical issue reports are involved in our studies, originating from the evolution of five proprietary systems for two companies. Our results show that RSSEs for both IA and CIA can help developers navigate large software projects, in terms of locating development teams and software artifacts. Finally, we discuss how to support the transfer of our results to industry, focusing on addressing the context dependency of our tool support by systematically tuning parameters to a specific operational setting

VTrace-A Tool for Visualizing Traceability Links Among Software Artefacts for an Evolving System

Traceability Management plays a key role in tracing the life of a requirement through all the specifications produced during the development phase of a software project. A lack of traceability information not only hinders the understanding of the system but also will prove to be a bottleneck in the future maintenance of the system. Projects that maintain traceability information during the development stages somehow fail to upgrade their artefacts or maintain traceability among the different versions of the artefacts that are produced during the maintenance phase. As a result the software artefacts lose the trustworthiness and engineers mostly work from the source code for impact analysis. The goal of our research is on understanding the impact of visualizing traceability links on change management tasks for an evolving system. As part of our research we have implemented a Traceability Visualization Tool-VTrace that manages software artefacts and also enables the visualization of traceability links. The results of our controlled experiment show that subjects who used the tool were more accurate and faster on change management tasks than subjects that didn't use the tool

VTrace-A Tool for Visualizing Traceability Links among Software Artefacts for an Evolving System

Traceability Management plays a key role in tracing the life of a requirement through all the specifications produced during the development phase of a software project. A lack of traceability information not only hinders the understanding of the system but also will prove to be a bottleneck in the future maintenance of the system. Projects that maintain traceability information during the development stages somehow fail to upgrade their artefacts or maintain traceability among the different versions of the artefacts that are produced during the maintenance phase. As a result the software artefacts lose the trustworthiness and engineers mostly work from the source code for impact analysis. The goal of our research is on understanding the impact of visualizing traceability links on change management tasks for an evolving system. As part of our research we have implemented a Traceability Visualization Tool-VTrace that manages software artefacts and also enables the visualization of traceability links. The results of our controlled experiment show that subjects who used the tool were more accurate and faster on change management tasks than subjects that didn’t use the tool

Towards an Intelligent System for Software Traceability Datasets Generation

Software datasets and artifacts play a crucial role in advancing automated software traceability research. They can be used by researchers in different ways to develop or validate new automated approaches. Software artifacts, other than source code and issue tracking entities, can also provide a great deal of insight into a software system and facilitate knowledge sharing and information reuse. The diversity and quality of the datasets and artifacts within a research community have a significant impact on the accuracy, generalizability, and reproducibility of the results and consequently on the usefulness and practicality of the techniques under study. Collecting and assessing the quality of such datasets are not trivial tasks and have been reported as an obstacle by many researchers in the domain of software engineering. In this dissertation, we report our empirical work that aims to automatically generate and assess the quality of such datasets. Our goal is to introduce an intelligent system that can help researchers in the domain of software traceability in obtaining high-quality “training sets”, “testing sets” or appropriate “case studies” from open source repositories based on their needs. In the first project, we present a first-of-its-kind study to review and assess the datasets that have been used in software traceability research over the last fifteen years. It presents and articulates the current status of these datasets, their characteristics, and their threats to validity. Second, this dissertation introduces a Traceability-Dataset Quality Assessment (T-DQA) framework to categorize software traceability datasets and assist researchers to select appropriate datasets for their research based on different characteristics of the datasets and the context in which those datasets will be used. Third, we present the results of an empirical study with limited scope to generate datasets using three baseline approaches for the creation of training data. These approaches are (i) Expert-Based, (ii) Automated Web-Mining, which generates training sets by automatically mining tactic\u27s APIs from technical programming websites, and lastly, (iii) Automated Big-Data Analysis, which mines ultra-large-scale code repositories to generate training sets. We compare the trace-link creation accuracy achieved using each of these three baseline approaches and discuss the costs and benefits associated with them. Additionally, in a separate study, we investigate the impact of training set size on the accuracy of recovering trace links. Finally, we conduct a large-scale study to identify which types of software artifacts are produced by a wide variety of open-source projects at different levels of granularity. Then we propose an automated approach based on Machine Learning techniques to identify various types of software artifacts. Through a set of experiments, we report and compare the performance of these algorithms when applied to software artifacts. Finally, we conducted a study to understand how software traceability experts and practitioners evaluate the quality of their datasets. In addition, we aim at gathering experts’ opinions on all quality attributes and metrics proposed by T-DQA