Recovering Traceability Links between Code and Documentation for Enterprise Project Artifacts

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.

Recovering traceability links between source code and software document can be very helpful for Software Maintenance and Software Reuse. Existing work has already achieved good results in extracting code elements (classes, methods, etc.) from software documents. However, it will lead to a lot of noise links if we link a document to all the code elements existing in it. In this paper, we propose an approach to identify the contextual code elements and the salient code elements in a software document, then we can weight the traceability links between source code and software document so that those noise traceability links can be filtered effectively. We measure the saliency of each code element in a document with four kinds of document-related features and three kinds of code-related features, and we adopt TransR-based code embedding technology to evaluate the distance between code elements. In the experiments, we get a precision of 70.7% in recognizing salient code elements of StackOverflow answer documents, which is more than 12% improvement compared with Rigby's work. At the same time, we can filter about 56.5%~69.3% noise traceability links compared with the RecoDoc approach. It will improve the quality of traceability links between source code and related software documents.

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.

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.

Term mismatch is a common limitation of traditional information retrieval (IR) models where relevance scores are estimated based on exact matching of documents and queries. Typically, good IR model should consider distinct but semantically similar words in the matching process. In this paper, we propose a method to incorporate word embedding (WE) semantic similarities into existing probabilistic IR models for Arabic in order to deal with term mismatch. Experiments are performed on the standard Arabic TREC collection using three neural word embedding models. The results show that extending the existing IR models improves significantly baseline bag-of-words models. Although the proposed extensions significantly outperform their baseline bag-of-words, the difference between the evaluated neural word embedding models is not statistically significant. Moreover, the overall comparison results show that our extensions significantly improve the Arabic WordNet based semantic indexing approach and three recent WE-based IR language models.

In this study we empirically investigate the adaptation of Test-Driven Development (TDD) practice into software Reverse Engineering (RE) process. We call this adaptation as Test-Driven Reverse Engineering (TDRE) process. We propose a two-layer prioritization process, which firstly prioritizes the already-implemented functionalities using the Cumulative Voting (CV) method and three prioritization criteria (importance, complexity and dependency), and secondly prioritizes test-cases for each prioritized functionality, using the same criteria. We conducted a case study in academia with students to empirically evaluate the usability and effectiveness of the prioritization process and the TDD adaptation into RE process. The results have shown that students with a good performance in testing had also good performance in designing UML class-diagrams. Moreover, the implementation of hierarchical test-cases for the already prioritized functionalities, improves code comprehension and redesigning in the RE process in terms of better total grades obtained.

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.

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.

The problem of consistency between software requirements and their implementation in source code becomes critically important as the scale and complexity of modern software systems increase, since the absence of reliable traceability links often leads to incomplete requirement implementation, complicates code maintenance, and hinders the verification of system correctness. Manual generation of traceability matrices is a labour-intensive and error-prone process, especially in large projects. The use of large language models opens up new opportunities for automating this process, as such models are capable of reflecting deep semantic relationships between text requirements and software code fragments. The article proposes a method for identifying traceability links between requirements and software code using transformer models of large language systems. The proposed approach is based on converting text artefacts into vector representations using CodeBERT, SBERT, and TF-IDF models, followed by calculating semantic similarity to automatically identify potential connections. The method covers such stages as data preparation, embedding generation, search for relevant fragments, and evaluation of the results obtained. The experiments were conducted on the MSR-2021 dataset, which contains real traceability links for several projects. The obtained results demonstrated the advantage of CodeBERT over traditional approaches (TF-IDF, SBERT): the method achieves accuracy of up to 0.85 and an F1-score of up to 0.50, depending on the search depth, which represents strong performance for automated information retrieval and ranking tasks. The study additionally confirmed the importance of considering the structural context of the code and showed the influence of the Top-K parameter on the balance between recall and precision. The results indicate that integrating LLM-based models significantly improves the level of automation, consistency, and quality of requirements traceability in modern software development environments.

Not many XML information retrieval (IR) systems exist that allow easy addition of and switching between different IR models. Especially for the scientific environment where building a system takes a lot of time and keeps researchers away from the real work, i.e., investigating what is the most effective IR model, a platform that would provide this functionality would be ideal. For this reason we developed such an XML IR system. It is centered around a logical algebra, named score region algebra (SRA), that enables transparent specification of IR models for XML databases (see [1] for more details). The transparency is achieved by a possibility to instantiate various retrieval models, using abstract score functions within algebra operators, while logical query plan and operator definitions remain unchanged. Our algebra operators model three important aspects of XML IR: element relevance score computation, element score propagation, and element score combination. To implement a new IR model, one only needs to provide definitions for these abstract function classes. To illustrate the usefulness of our algebra our demo system supports several, well known IR scoring models (e.g., Language Models, Okapi, and tf.idf), combined with different score propagation and combination functions. The user can select which model to use at run time. Following good practice in database systems design, our prototype system has a typical three-layered architecture. (1) The conceptual layer takes a NEXI [3] query expression as input, e.g.,

The reverse engineering process in development is widely used to carry out the reverse engineering process for the design of certain products, in this study the reverse engineering process on the palm oil lorry wheel is to improve the design as well as a combination of materials to increase the life of the lorry wheel product in the palm oil processing. The process of scanning the surface of the lorry wheel using camera and the reverse engineering process is carried out by measuring the precision of the lorry wheel by measuring using a precision measuring instrument, data obtained by measurement and reconstruction model. data obtained by measurement and reconstruction model using CAD software and CAE software used to analyze the CAD and CAE software are used to analyze the design results after the reverse engineering process. reverse engineering process. So, it is necessary to develop a reverse engineering system design that will be useful in terms of efficiency and service life of the lorry wheel, in the design of this lorry wheel uses CAD and CAM software to model the lorry wheels with simulation and analysis systems. The next process is to modify and remanufacture the lorry wheel model using machine tools so that the prototype of the manufacturing process can be tested. so that the prototype of the manufacturing process can be tested on the lorry to see the durability of the reverse engineering product. to see the durability of the reverse engineering product. The results of this research showed that the reverse engineering data can be interpolated for further CAE process

Tracking a variety of traceability links between artifacts assists software developers in comprehension, efficient development, and effective management of a system. Traceability systems to date based on various Information Retrieval (IR) techniques have been faced with a major open research challenge: how to extract these links with both high precision and high recall. In this paper we describe an experimental approach that combines Regular Expression, Key Phrases, and Clustering with IR techniques to enhance the performance of IR for traceability link recovery between documents and source code. Our preliminary experimental results show that our combination technique improves the performance of IR, increases the precision of retrieved links, and recovers more true links than IR alone.

Enterprise content management is quite an issue because of heterogeneity, complexity and size. Moreover, this burden is exponentially growing. In order to efficiently handle the enterprise content, a systematic approach is required, which embraces object-based models and software engineering tools. The paper outlines the approach, and gives the primary attention to the formal models for content management. The formal models are fully supported by problem-oriented languages and CASE level tools. The proposed approach has been extended for application in project integration management. The paper also suggests the method of dynamic modeling of planning and change management processes taking into account particular project constraints, priorities, environment etc. This method is based on ER-model of project structures (WBS, OBS, TRM, network etc.) and the Selective-function Linear resolution for Definite Clause. The resolution procedure defines the sequence of the project structures determination i.e. the management process including inputs and outputs on the basis of specified initial conditions. This method was applied in a consulting project in public management domain. It could be implemented as a web-service for another web-based Enterprise Project Management Systems or as a stand-alone solution with GUI. The approach has been approved by a number of successful enterprise-scale implementations in oil-and-gas industry, public management, trading and banking enterprises, nuclear power plant construction and other areas.

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.