Improving Requirements Tracing via Information Retrieval

Abstrac

Grand Challenges of Traceability: The Next Ten Years

In 2007, the software and systems traceability community met at the first Natural Bridge symposium on the Grand Challenges of Traceability to establish and address research goals for achieving effective, trustworthy, and ubiquitous traceability. Ten years later, in 2017, the community came together to evaluate a decade of progress towards achieving these goals. These proceedings document some of that progress. They include a series of short position papers, representing current work in the community organized across four process axes of traceability practice. The sessions covered topics from Trace Strategizing, Trace Link Creation and Evolution, Trace Link Usage, real-world applications of Traceability, and Traceability Datasets and benchmarks. Two breakout groups focused on the importance of creating and sharing traceability datasets within the research community, and discussed challenges related to the adoption of tracing techniques in industrial practice. Members of the research community are engaged in many active, ongoing, and impactful research projects. Our hope is that ten years from now we will be able to look back at a productive decade of research and claim that we have achieved the overarching Grand Challenge of Traceability, which seeks for traceability to be always present, built into the engineering process, and for it to have "effectively disappeared without a trace". We hope that others will see the potential that traceability has for empowering software and systems engineers to develop higher-quality products at increasing levels of complexity and scale, and that they will join the active community of Software and Systems traceability researchers as we move forward into the next decade of research

Grand Challenges of Traceability: The Next Ten Years

In 2007, the software and systems traceability community met at the first Natural Bridge symposium on the Grand Challenges of Traceability to establish and address research goals for achieving effective, trustworthy, and ubiquitous traceability. Ten years later, in 2017, the community came together to evaluate a decade of progress towards achieving these goals. These proceedings document some of that progress. They include a series of short position papers, representing current work in the community organized across four process axes of traceability practice. The sessions covered topics from Trace Strategizing, Trace Link Creation and Evolution, Trace Link Usage, real-world applications of Traceability, and Traceability Datasets and benchmarks. Two breakout groups focused on the importance of creating and sharing traceability datasets within the research community, and discussed challenges related to the adoption of tracing techniques in industrial practice. Members of the research community are engaged in many active, ongoing, and impactful research projects. Our hope is that ten years from now we will be able to look back at a productive decade of research and claim that we have achieved the overarching Grand Challenge of Traceability, which seeks for traceability to be always present, built into the engineering process, and for it to have "effectively disappeared without a trace". We hope that others will see the potential that traceability has for empowering software and systems engineers to develop higher-quality products at increasing levels of complexity and scale, and that they will join the active community of Software and Systems traceability researchers as we move forward into the next decade of research

Software Engineers' Information Seeking Behavior in Change Impact Analysis - An Interview Study

Software engineers working in large projects must navigate complex information landscapes. Change Impact Analysis (CIA) is a task that relies on engineers' successful information seeking in databases storing, e.g., source code, requirements, design descriptions, and test case specifications. Several previous approaches to support information seeking are task-specific, thus understanding engineers' seeking behavior in specific tasks is fundamental. We present an industrial case study on how engineers seek information in CIA, with a particular focus on traceability and development artifacts that are not source code. We show that engineers have different information seeking behavior, and that some do not consider traceability particularly useful when conducting CIA. Furthermore, we observe a tendency for engineers to prefer less rigid types of support rather than formal approaches, i.e., engineers value support that allows flexibility in how to practically conduct CIA. Finally, due to diverse information seeking behavior, we argue that future CIA support should embrace individual preferences to identify change impact by empowering several seeking alternatives, including searching, browsing, and tracing.Comment: Accepted for publication in the proceedings of the 25th International Conference on Program Comprehensio

GIS Application to Support Land Administration Services in Ghana: Institutional Factors and Software Developments

In June 1999, the Ghanaian Government launched a new land policy document that sought to address some fundamental problems associated with land administration and management in the country. The document identified the weak land administration system as a particular problem and recommended the introduction of computer-aided information systems in the ‘lands sector’. In 2001, the Government made further proposals to prepare and implement a Land Administration Programme (LAP) to provide a better platform for evolving an efficient land administration that would translate the ‘National Land Policy’ into action. Thus, an up-to-date land information system (LIS), supporting efficient management of land records, is to be constructed, which provides a context for the research reported in this paper. We document two aspects of our research on the adoption of GIS by the Lands Commission Secretariat (LCS) which form part of a pilot project in GIS diffusion. Part one of the paper mainly outlines the empirical results arising from fieldwork undertaken during 2001 to determine the information and GIS requirements of the LCS in relation to their routine administrative processes and to identify the critical factors that are required to ensure that any new GIS applications are successfully embraced. Part two explains the prototype software system developed using ArcView 3.2 and Access that provides the LCS with a means to automate some of the routine administrative tasks that they are required to fulfil. The software has been modified and upgraded following an initial evaluation by LCS employees also conducted as part of the fieldwork in Accra

Improving Automated Requirements Trace Retrieval Through Term-Based Enhancement Strategies

Requirements traceability is concerned with managing and documenting the life of requirements. Its primary goal is to support critical software development activities such as evaluating whether a generated software system satisfies the specified set of requirements, checking that all requirements have been implemented by the end of the lifecycle, and analyzing the impact of proposed changes on the system. Various approaches for improving requirements traceability practices have been proposed in recent years. Automated traceability methods that utilize information retrieval (IR) techniques have been recognized to effectively support the trace generation and retrieval process. IR based approaches not only significantly reduce human effort involved in manual trace generation and maintenance, but also allow the analyst to perform tracing on an “as-needed” basis. The IR-based automated traceability tools typically retrieve a large number of potentially relevant traceability links between requirements and other software artifacts in order to return to the analyst as many true links as possible. As a result, the precision of the retrieval results is generally low and the analyst often needs to manually filter out a large amount of unwanted links. The low precision among the retrieved links consequently impacts the usefulness of the IR-based tools. The analyst’s confidence in the effectiveness of the approach can be negatively affected both by the presence of a large number of incorrectly retrieved traces, and the number of true traces that are missed. In this thesis we present three enhancement strategies that aim to improve precision in trace retrieval results while still striving to retrieve a large number of traceability links. The three strategies are: 1) Query term coverage (TC) This strategy assumes that a software artifact sharing a larger proportion of distinct words with a requirement is more likely to be relevant to that requirement. This concept is defined as query term coverage (TC). A new approach is introduced to incorporate the TC factor into the basic IR model such that the relevance ranking for query-document pairs that share two or more distinct terms will be increased and the retrieval precision is improved. 2) Phrasing The standard IR models generate similarity scores for links between a query and a document based on the distribution of single terms in the document collection. Several studies in the general IR area have shown phrases can provide a more accurate description of document content and therefore lead to improvement in retrieval [21, 23, 52]. This thesis therefore presents an approach using phrase detection to enhance the basic IR model and to improve its retrieval accuracy. 3) Utilizing a project glossary Terms and phrases defined in the project glossary tend to capture the critical meaning of a project and therefore can be regarded as more meaningful for detecting relations between documents compared to other more general terms. A new enhancement technique is then introduced in this thesis that utilizes the information in the project glossary and increases the weights of terms and phrases included in the project glossary. This strategy aims at increasing the relevance ranking of documents containing glossary items and consequently at improving the retrieval precision. The incorporation of these three enhancement strategies into the basic IR model, both individually and synergistically, is presented. Extensive empirical studies have been conducted to analyze and compare the retrieval performance of the three strategies. In addition to the standard performance metrics used in IR, a new metric average precision change [80] is also introduced in this thesis to measure the accuracy of the retrieval techniques. Empirical results on datasets with various characteristics show that the three enhancement methods are generally effective in improving the retrieval results. The improvement is especially significant at the top of the retrieval results which contains the links that will be seen and inspected by the analyst first. Therefore the improvement is especially meaningful as it implies the analyst may be able to evaluate those important links earlier in the process. As the performance of these enhancement strategies varies from project to project, the thesis identifies a set of metrics as possible predictors for the effectiveness of these enhancement approaches. Two such predictors, namely average query term coverage (QTC) and average phrasal term coverage (PTC), are introduced for the TC and the phrasing approach respectively. These predictors can be employed to identify which enhancement algorithm should be used in the tracing tool to improve the retrieval performance for specific documents collections. Results of a small-scale study indicate that the predictor values can provide useful guidelines to select a specific tracing approach when there is no prior knowledge on a given project. The thesis also presents criteria for evaluating whether an existing project glossary can be used to enhance results in a given project. The project glossary approach will not be effective if the existing glossary is not being consistently followed in the software development. The thesis therefore presents a new procedure to automatically extract critical keywords and phrases from the requirements collection of a given project. The experimental results suggest that these extracted terms and phrases can be used effectively in lieu of missing or ineffective project glossary to help improve precision of the retrieval results. To summarize, the work presented in this thesis supports the development and application of automated tracing tools. The three strategies share the same goal of improving precision in the retrieval results to address the low precision problem, which is a big concern associated with the IR-based tracing methods. Furthermore, the predictors for individual enhancement strategies presented in this thesis can be utilized to identify which strategy will be effective in the specific tracing tasks. These predictors can be adopted to define intelligent tracing tools that can automatically determine which enhancement strategy should be applied in order to achieve the best retrieval results on the basis of the metrics values. A tracing tool incorporating one or more of these methods is expected to achieve higher precision in the trace retrieval results than the basic IR model. Such improvement will not only reduce the analyst’s effort of inspecting the retrieval results, but also increase his or her confidence in the accuracy of the tracing tool

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