Semantic Software Traceability Using Property Listing Task: Pilot Study

Software requirements traceability links have been widely recognized as an essential means for effective system evolution when requirements change. However, creating and maintaining traceability links is not an easy task in practice, especially when faced time pressure. Therefore, an automatic and accurate traceability link recovery approach is needed. Existing methods usually recover the traceability links through information retrieval models [1], which calculate text similarity among software artifacts. Although the text-similarity plays an essential role in correlating software artifacts, we argue that the context of software artifacts also renders important clues for establishing the traceability links among software artifacts. For example, for each software use case, its includes/extends use cases can be seen as its context information, contributing to comprehensively profiling the use case. The collection of software artifacts can be modeled as a graph structure through a variety of explicit relationships. Description-Embodied Knowledge Representation Learning (DKRL) [2] is a widely accepted method, which can effectively capture the structural information of explicit relationship and description information of entities. By effectively and precisely embedding such a graph, the context information can be meaningfully represented, contributing to the identification of requirements traceability links. In this paper, we propose a Traceability Link Recovery-Knowledge Representation Learning (TLR-KRL) to recover requirements traceability links between use cases and code based on DKRL. This work has been accepted in The 32nd International Conference on Software Engineering& Knowledge Engineering. TLR-KRL can comprehensively characterize software artifacts by embedding both text information and structural relationships, an overview of which is shown in Fig. 1. Specifically, we follow a systematic process to extend the DKRL model, improving its negative sampling method and thus minimizing false-negative samples generated by the original DKRL model. In such a way, we are able to obtain more precise embedding of software artifacts. Such meaningful embeddings are then used to train traceability link classifiers by using supervised machine learning algorithms. All traceability link candidates obtained from the classifier will be further screened by using triple classification in order to retrieve more correct traceability links. To verify the effectiveness of our method, we have carried out experiments on four datasets, including eTour, EAnci, Clinic, and ITrust. The evaluation results on each of the datasets have all shown that our approach can outperform existing work.

Maintaining traceability links among software artifacts is particularly important for many software engineering tasks. Even though automatic traceability link recovery tools are successful in identifying the semantic connections among software artifacts produced during software development, no existing traceability link management approach can effectively and automatically deal with software evolution. We propose a technique to automatically manage traceability link evolution and update the links in evolving software. Our novel technique, called incremental latent semantic indexing (iLSI), allows for the fast and low-cost LSI computation for the update of traceability links by analyzing the changes to software artifacts and by reusing the result from the previous LSI computation before the changes. We present our iLSI technique, and describe a complete automatic traceability link evolution management tool, TLEM, that is capable of interactively and quickly updating traceability links in the presence of evolving software artifacts. We report on our empirical evaluation with various experimental studies to assess the performance and usefulness of our approach.

A key part of software evolution and maintenance is the continuous integration from collaborative efforts, often resulting in complex traceability challenges between software artifacts: features and modules remain scattered in the source code, and traceability links become harder to recover. In this paper, we perform a systematic mapping study dealing with recent research recovering these links through information retrieval, with a particular focus on natural language processing (NLP).Our search strategy gathered a total of 96 papers in focus of our study, covering a period from 2013 to 2021. We conducted trend analysis on NLP techniques and tools involved, and traceability efforts (applying NLP) across the software development life cycle (SDLC). Based on our study, we have identified the following key issues, barriers, and setbacks: syntax convention, configuration, translation, explainability, properties representation, tacit knowledge dependency, scalability, and data availability.Based on these, we consolidated the following open challenges: representation similarity across artifacts, the effectiveness of NLP for traceability, and achieving scalable, adaptive, and explainable models. To address these challenges, we recommend a holistic framework for NLP solutions to achieve effective traceability and efforts in achieving interoperability and explainability in NLP models for traceability.

Traceability is defined by Center of Excellence for Software Traceability as the ability to interrelate any uniquely identifiable software engineering artifact to any other, maintain required links over time, and use resulting network to answer questions of both software product and its development process. It is an essential element in software development process, especially for Safety-Critical systems where trace links are used to demonstrate that all hazards and faults are mitigated in delivered system. Maintaining trace links as a software system evolves over time is difficult, error-prone and costly. As a result, trace links tend to become outdated and safety-cases, which rely on trace links and associated data to compose arguments and evidence for system safety are invalidated. In practice, software developers must therefore painstakingly update trace links in order to re-validate safety-cases whenever they seek re-certification of their product. While a significant body of work exists in area of trace link creation, very limited research has focused on problem of link evolution. The goal of this dissertation is therefore to deliver an accurate, scalable, and practical approach for supporting trace link evolution across a diverse set of software artifacts including requirements, source code and safety cases.

In Model Driven Engineering, traceability is used to establish relationships between various software artifacts during the software life cycle. Traceability can be also used to define dependencies between related elements in different models, to propagate and verify properties from one model to another and to analyze the impact of changes. In this paper we describe how to define typed trace-links between different kinds of models in our model transformation chain PUMA4SOA, which generates Layered Queuing performance models from UML software models of service-oriented applications. The goal of PUMA4SOA is to help evaluate the performance of SOA systems in the early development phases. In our approach, the traceability links are stored externally in a new model, which maintain traces separately from the source and target models they refer to. We illustrate how traceability links can be used to propagate the results of the performance model back to the original software model.

Maintaining traceability links of software systems is a crucial task for software management and development. Unfortunately, dealing with traceability links are typically taken as afterthought due to time pressure. Some studies attempt to use information retrieval-based methods to automate this task, but they only concentrate on calculating the textual similarity between various software artifacts and do not take into account the properties of such artifacts. In this paper, we propose a novel traceability link recovery approach, which comprehensively measures the similarity between use cases and source code by exploring their particular properties. To this end, we leverage and combine machine learning and logical reasoning techniques. On the one hand, our method extracts features by considering the semantics of the use cases and source code, and uses a classification algorithm to train the classifier. On the other hand, we utilize the relationships between artifacts and define a series of rules to recover traceability links. In particular, we not only leverage source code’s structural information, but also take into account the interrelationships between use cases. We have conducted a series of experiments on multiple datasets to evaluate our approach against existing approaches, the results of which show that our approach is substantially better than other methods.

Traceability is the glue that holds together software artifacts at different levels of abstraction, from system design to software requirements. Traceability links ensure that code is maintainable and can minimize faults stemming from improper implementations, misunderstood requirements, and unexecuted test cases. Effectively, traceability ensures that all aspects of the software design life cycle are interconnected for both assurance and certification purposes. For big data applications, traceability remains a critical activity to ensure that the system can adequately support each big data dimension (i.e., “V”). For instance, a big data application may be subject to unexpected combinations of data types in a JSON packet that may result in an exception error, or alternatively, the rate at which data is received may be faster than the system can support, resulting in dropped packets. This chapter focuses on software traceability in the presence of big data, comprising topics in software traceability, link discovery, and link maintenance.

When a system evolves, while the source code is changed, documentation and traceability links are barely ever updated: maintaining traceability information between software artifacts is a costly and tedious activity, which is frequently sacrificed during development and maintenance due to market pressure. The paper presents a new method to recovery traceability links between high level and low level artifacts. The method is based on the partial knowledge of a subset of tracability links. It can be fully automated and the human intervention is only required to confirm or confute recovered traceability links. The method has been applied to software written in Java, to trace classes onto functional requirements. Experimental results demonstrate the superiority of the novel method over previously published results on the same system.

Developers are the most important resource to build and maintain software projects. Due to various reasons, some developers take more responsibility, and this type of developers are more valuable and indispensable for the project. Without them, the success of the project would be at risk. We use the term key developers for these essential and valuable developers, and identifying them is a crucial task for managerial decisions such as risk assessment for potential developer resignations. We study key developers under three categories: jacks, mavens and connectors. A typical jack (of all trades) has a broad knowledge of the project, they are familiar with different parts of the source code, whereas mavens represent the developers who are the sole experts in specific parts of the projects. Connectors are the developers who involve different groups of developers or teams. They are like bridges between teams.

The ever-increasing globalization of the software industry presents challenges related to requirements engineering activities. Managing requirements’ changes and tracing software artifacts is not trivial in a multi-site environment composed of a variety of stakeholders that do not trust each other. In this study, we propose a neural blockchain prototype for the traceability of requirements (BC4RT) throughout the software development lifecycle in interorganizational software projects. The prototype is implemented using a neural blockchain platform, namely NDL ArcaNet, due to its inherent properties: performance efficiency, sustainability, and scalability. Besides these features, the proposed prototype provides a holistic and reliable view of software artifacts, requirements’ changes, and trace links. The increased visibility enhances collaboration, communication, and trust among stakeholders, and can potentially improve software development efficiency and quality.KeywordsBlockchain technologyRequirements traceabilityInterorganizational software projectsNeural distributed ledger

Information retrieval (IR) approaches have proven useful in recovering traceability links between free-text documentation and source code. IR-based traceability recovery approaches produce ranked lists of traceability links between pieces of documentation and source code. These traceability links are then pruned using various strategies and, finally, validated by human experts. In this paper we propose two contributions to improve the precision and recall of traceability links and, thus, reduces the required human experts' manual validation effort. First, we propose a novel approach, Trust race, inspired by Web trust models to improve the precision and recall of traceability links: Trust race uses intractability recovery approach to obtain a set of traceability links, which rankings are then re-evaluated using a set of other traceability recovery approaches. Second, we propose a novel traceability recovery approach, His trace, to identify traceability links between requirements and source code through CVS/SVN change logs using a Vector Space Model (VSM). We combine a traditional recovery traceability approach with His trace to build Trust race in which we use Histraceas one expert adding knowledge to the traceability links extracttedfrom CVS/SVN change logs. We apply Trustrace on two case studies to compare its traceability links with those recovered using only the VSM-based approach, in terms of precision and recall. We show that Trustrace improves with statistical significance the precision of the traceability links while also improving recall but without statistical significance.

Previous work has established both the importance and difficulty of establishing and maintaining adequate software traceability. While it has been shown to support essential maintenance and evolution tasks, recovering traceability links between related software artifacts is a time consuming and error prone task. As such, substantial research has been done to reduce this barrier to adoption by at least partially automating traceability link recovery. In particular, recent work has shown that supervised machine learning can be effectively used for automating traceability link recovery, as long as there is sufficient data (i.e., labeled traceability links) to train a classification model. Unfortunately, the amount of data required by these techniques is a serious limitation, given that most software systems rarely have traceability information to begin with. In this paper we address this limitation of previous work and propose an approach based on active learning, which substantially reduces the amount of training data needed by supervised classification approaches for traceability link recovery while maintaining similar performance.

The traceability links among software artifacts plays a very important role for the maintenance of consistency in the evolution of software product lines. This paper introduces an improved approach to the recovery of traceability information between requirement documents and source codes based on Latent Semantic Indexing (LSI) and the special features of object-oriented source codes. In order to obtain accurate traceability links, it employs the hierarchical information caused by the inheritance relationship among classes and recovers the traceability links using class clusters. Moreover, it assigns different weights for the terms in source codes according to their degrees of correlation to the documents. The case demonstrates the improved approach, and the experimental results show that it can increase the extraction precision by 3%~6%, compared with that based on the traditional LSI.

Information retrieval (IR) is widely used in automatically requirements traceability recovery. Corresponding approaches are built based on textual similarity, that is, the higher the similarity, the higher possibility of artifacts related. A common work of many IR-based techniques is to remove false positive links in the candidate links to achieve higher accuracy. In fact, traceability links can be recovered by different kinds of information, not only the textual information. In our study, we propose to recover more traceability links by exploring both textual features and structural information. Specifically, we use combined IR techniques to process the textual information of the software artifacts, and extract the structural information from the source code, establishing corresponding code relationship graphs. We then incorporate such structural information into the traceability recovery analysis by using graph embedding. The results show that combined IR techniques and using graph embedding technology to process structural information can improve the recovery traceability.

Context and motivation: Traceability is used to follow and understand the relationships between various software engineering artifacts such as requirements and source code. Comprehensive traceability of software engineering artifacts is important to ensure that a software can be further developed or maintained. Traceability links are often created manually by using commit ids or retrospectively by using information retrieval (IR). Question/Problem: However, creating traceability links manually is costly and it is error-prone in retrospect. As part of our ongoing research on feature management where traceability is also of interest, we use a lightweight tagging approach to relate artifacts. We want to investigate how such a tag-based approach compares to approaches using commit ids, interaction logs (IL), and IR for creating traceability links. Principal ideas/results: We conducted a case study in which students applied the tag-based, the IL-based, and the commit-based approach. We transformed the tags to traceability links and compared them with the commit-based and IL-based approach as well as with IR-based approaches, using a gold standard. We applied different improvements. Contribution: This is the first study comparing four completely different traceability approaches in one project. The combination of IL and commit ids shows the best precision and recall but is intrusive. The other approaches differ less in precision and recall and both need improvement for practical application. We discuss the benefits and drawbacks of the different approaches and state implications for research and practice.