Traceability Links Research Articles

Software Dependency Estimation in the code Repositories for the Requirement Evolution

Dependency is the only means to ensure that the source code of a system is consistent with its requirements. During software maintenance and evolution, requirement dependency links become obsolete because dependency model is been not trained properly to updating them. Yet, recovering these dependency links later is a daunting and costly task for building the model for unsupervised enhancements. Consequently, the literature has proposed methods, techniques, and tools to recover these dependency links semi-automatically or automatically. Among the proposed techniques, the literature showed that information retrieval (IR) techniques can automatically recover traceability links between free-text requirements and source code through classification techniques to the Software repositories. However, IR techniques lack accuracy (precision and recall) in terms of Text and concept based mining also leads to code sense disambiguation. In this paper, we show that Semantic mining of software repositories and combining mined results with IR can improve the accuracy (precision and recall) of IR techniques. We apply Dependency Estimation on to compare the accuracy of its dependency links with those recovered using state-of-the-art IR techniques from Vector Space model and Concept based mining. We thus show that mining software repositories and combining the mined data with existing results from IR techniques improves the precision and recall of requirement dependency links.

Semi-automatic architectural pattern identification and documentation using architectural primitives

In this article, we propose an interactive approach for the semi-automatic identification and documentation of architectural patterns based on a domain-specific language. To address the rich concepts and variations of patterns, we firstly propose to support pattern description through architectural primitives. These are primitive abstractions at the architectural level that can be found in realizations of multiple patterns, and they can be leveraged by software architects for pattern annotation during software architecture documentation or reconstruction. Secondly, using these annotations, our approach automatically suggests possible pattern instances based on a reusable catalog of patterns and their variants. Once a pattern instance has been documented, the annotated component models and the source code get automatically checked for consistency and traceability links are automatically generated. To study the practical applicability and performance of our approach, we have conducted three case studies for existing, non-trivial open source systems.

Horizontal traceability for just-in-time requirements: the case for open source feature requests

Agile projects typically employ just-in-time requirements engineering and record their requirements so-called feature requests in an issue tracker. In open source projects, we observed large networks of feature requests that are linked to each other. Both when trying to understand the current state of the system and to understand how a new feature request should be implemented, it is important to know and understand all these tightly related feature requests. However, we still lack tool support to visualize and navigate these networks of feature requests. A first step in this direction is to see whether we can identify additional links that are not made explicit in the feature requests, by measuring the text-based similarity with a vector space model VSM using term frequency-inverse document frequency TF-IDF as a weighting factor. We show that a high text-based similarity score is a good indication for related feature requests. This means that with a TF-IDF VSM, we can establish horizontal traceability links, thereby providing new insights for users or developers exploring the feature request space. Copyright © 2014 John Wiley & Sons, Ltd.

Query-driven soft traceability links for models

Model repositories play a central role in the model driven development of complex software-intensive systems by offering means to persist and manipulate models obtained from heterogeneous languages and tools. Complex models can be assembled by interconnecting model fragments by hard links, i.e., regular references, where the target end points to external resources using storage-specific identifiers. This approach, in certain application scenarios, may prove to be a too rigid and error prone way of interlinking models. As a flexible alternative, we propose to combine derived features with advanced incremental model queries as means for soft interlinking of model elements residing in different model resources. These soft links can be calculated on-demand with graceful handling for temporarily unresolved references. In the background, the links are maintained efficiently and flexibly by using incremental model query evaluation. The approach is applicable to modeling environments or even property graphs for representing query results as first-class relations, which also allows the chaining of soft links that is useful for modular applications. The approach is evaluated using the Eclipse Modeling Framework (EMF) and EMF-IncQuery in two complex industrial case studies. The first case study is motivated by a knowledge management project from the financial domain, involving a complex interlinked structure of concept and business process models. The second case study is set in the avionics domain with strict traceability requirements enforced by certification standards (DO-178b). It consists of multiple domain models describing the allocation scenario of software functions to hardware components.

An empirical study on the importance of source code entities for requirements traceability

Requirements Traceability (RT) links help developers during program comprehension and maintenance tasks. However, creating RT links is a laborious and resource-consuming task. Information Retrieval (IR) techniques are useful to automatically create traceability links. However, IR-based techniques typically have low accuracy (precision, recall, or both) and thus, creating RT links remains a human intensive process. We conjecture that understanding how developers verify RT links could help improve the accuracy of IR-based RT techniques to create RT links. Consequently, we perform an empirical study consisting of four case studies. First, we use an eye-tracking system to capture developers' eye movements while they verify RT links. We analyse the obtained data to identify and rank developers' preferred types of Source Code Entities (SCEs), e.g., domain vs. implementation-level source code terms and class names vs. method names. Second, we perform another eye-tracking case study to confirm that it is the semantic content of the developers' preferred types of SCEs and not their locations that attract developers' attention and help them in their task to verify RT links. Third, we propose an improved term weighting scheme, i.e., Developers Preferred Term Frequency/Inverse Document Frequency (D P T F / I D F), that uses the knowledge of the developers' preferred types of SCEs to give more importance to these SCEs into the term weighting scheme. We integrate thisweighting scheme with an IR technique, i.e., Latent Semantic Indexing (LSI), to create a new technique to RT link recovery. Using three systems (iTrust, Lucene, and Pooka), we show that the proposed technique statistically improves the accuracy of the recovered RT links over a technique based on LSI and the usual Term Frequency/Inverse Document Frequency (T F / I D F) weighting scheme. Finally, we compare the newly proposed D P T F / I D F with our original Domain Or Implementation/Inverse Document Frequency (D O I / I D F) weighting scheme.

Touch Arithmetic: A process-based Computer-Aided Assessment approach for capture of problem solving steps in the context of elementary mathematics

Technology today offers many new opportunities for innovation in educational assessment and feedback through rich assessment tasks, efficient scoring and reporting. However many Computer-Aided Assessment (CAA) environments focus on grading and providing feedback on the final product of assessment tasks rather than the process of problem solving. Focussing on steps and problem-solving processes can help teachers to diagnose strengths and weaknesses, discover strategies, and to provide appropriate feedback. This study explores a method that uses trace links on an interactive touch-based computing tool for the capture and analysis of solution steps in elementary mathematics. The tool was evaluated in an observational study among 8 and 9 year old primary school children (N = 39). The approach yielded similar performance scores as compared to paper-and-pencil tests while providing more explicit information on the problem-solving process. The output data was useful for scoring intermediate and final answers as well as feedback information on types and time efficiencies of strategies used. An implication of this study for teachers and researchers is that they can more accurately assess students' understanding of important concepts, and be in a better position to provide rich and detailed feedback while motivating students with interactive tasks.

On the role of semantics in automated requirements tracing

In this paper, we investigate the potential benefits of utilizing natural language semantics in automated traceability link retrieval. In particular, we evaluate the performance of a wide spectrum of semantically enabled information retrieval methods in capturing and presenting requirements traceability links in software systems. Our objectives are to gain more operational insights into these methods and to provide practical guidelines for the design and development of effective requirements tracing and management tools. To achieve our research objectives, we conduct an experimental analysis using three datasets from various application domains. Results show that considering more semantic relations in traceability link retrieval does not necessarily lead to higher quality results. Instead, a more focused semantic support, that targets specific semantic relations, is expected to have a greater impact on the overall performance of tracing tools. In addition, our analysis shows that explicit semantic methods, that exploit local or domain-specific sources of knowledge, often achieve a more satisfactory performance than latent methods, or methods that derive semantics from external or general-purpose knowledge sources.

Supporting requirements to code traceability through refactoring

In this paper, we hypothesize that the distorted traceability tracks of a software system can be systematically re-established through refactoring, a set of behavior-preserving transformations for keeping the system quality under control during evolution. To test our hypothesis, we conduct an experimental analysis using three requirements-to-code datasets from various application domains. Our objective is to assess the impact of various refactoring methods on the performance of automated tracing tools based on information retrieval. Results show that renaming inconsistently named code identifiers, using Rename Identifier refactoring, often leads to improvements in traceability. In contrast, removing code clones, using eXtract Method (XM) refactoring, is found to be detrimental. In addition, results show that moving misplaced code fragments, using Move Method refactoring, has no significant impact on trace link retrieval. We further evaluate Rename Identifier refactoring by comparing its performance with other strategies often used to overcome the vocabulary mismatch problem in software artifacts. In addition, we propose and evaluate various techniques to mitigate the negative impact of XM refactoring. An effective traceability sign analysis is also conducted to quantify the effect of these refactoring methods on the vocabulary structure of software systems.

Recovering test-to-code traceability using slicing and textual analysis

Test suites are a valuable source of up-to-date documentation as developers continuously modify them to reflect changes in the production code and preserve an effective regression suite. While maintaining traceability links between unit test and the classes under test can be useful to selectively retest code after a change, the value of having traceability links goes far beyond this potential savings. One key use is to help developers better comprehend the dependencies between tests and classes and help maintain consistency during refactoring. Despite its importance, test-to-code traceability is not common in software development and, when needed, traceability information has to be recovered during software development and evolution. We propose an advanced approach, named SCOTCH+ (Source code and COncept based Test to Code traceability Hunter), to support the developer during the identification of links between unit tests and tested classes. Given a test class, represented by a JUnit class, the approach first exploits dynamic slicing to identify a set of candidate tested classes. Then, external and internal textual information associated with the classes retrieved by slicing is analyzed to refine this set of classes and identify the final set of candidate tested classes. The external information is derived from the analysis of the class name, while internal information is derived from identifiers and comments. The approach is evaluated on five software systems. The results indicate that the accuracy of the proposed approach far exceeds the leading techniques found in the literature.

Semi-automated architectural abstraction specifications for supporting software evolution

In this paper we present an approach for supporting the semi-automated architectural abstraction of architectural models throughout the software life-cycle. It addresses the problem that the design and implementation of a software system often drift apart as software systems evolve, leading to architectural knowledge evaporation. Our approach provides concepts and tool support for the semi-automatic abstraction of architecture component and connector views from implemented systems and keeping the abstracted architecture models up-to-date during software evolution. In particular, we propose architecture abstraction concepts that are supported through a domain-specific language (DSL). Our main focus is on providing architectural abstraction specifications in the DSL that only need to be changed, if the architecture changes, but can tolerate non-architectural changes in the underlying source code. Once the software architect has defined an architectural abstraction in the DSL, we can automatically generate architectural component views from the source code using model-driven development (MDD) techniques and check whether architectural design constraints are fulfilled by these models. Our approach supports the automatic generation of traceability links between source code elements and architectural abstractions using MDD techniques to enable software architects to easily link between components and the source code elements that realize them. It enables software architects to compare different versions of the generated architectural component view with each other. We evaluate our research results by studying the evolution of architectural abstractions in different consecutive versions of five open source systems and by analyzing the performance of our approach in these cases.

Enhancing software artefact traceability recovery processes with link count information

ContextThe intensive human effort needed to manually manage traceability information has increased the interest in using semi-automated traceability recovery techniques. In particular, Information Retrieval (IR) techniques have been largely employed in the last ten years to partially automate the traceability recovery process. AimPrevious studies mainly focused on the analysis of the performances of IR-based traceability recovery methods and several enhancing strategies have been proposed to improve their accuracy. Very few papers investigate how developers (i) use IR-based traceability recovery tools and (ii) analyse the list of suggested links to validate correct links or discard false positives. We focus on this issue and suggest exploiting link count information in IR-based traceability recovery tools to improve the performances of the developers during a traceability recovery process. MethodTwo empirical studies have been conducted to evaluate the usefulness of link count information. The two studies involved 135 University students that had to perform (with and without link count information) traceability recovery tasks on two software project repositories. Then, we evaluated the quality of the recovered traceability links in terms of links correctly and erroneously traced by the students. ResultsThe results achieved indicate that the use of link count information significantly increases the number of correct links identified by the participants. ConclusionsThe results can be used to derive guidelines on how to effectively use traceability recovery approaches and tools proposed in the literature.

Traceability Issue in PM2.5 and PM10 Measurements

Nowadays particle size and mass concentration measurements are the important parameter of the ambient air quality standards of several countries. The regulatory limits of mass concentration of particulate matter (PM) for the size classes of PM2.5 and PM10, i.e., particle sizes of less than or equal to 2.5 and 10 μm in aerodynamic diameter, respectively in air are defined on yearly and hourly time-weighted-average basis. However, these limits are different in different regulations of the countries. Both of the parameters relate with the human health, climate and other issues, therefore accurate and precise measurement of these parameters are very important. Despite this, so far not much work has progressed in national metrology institutes (NMIs) worldwide on calibration and traceability issue of PM measurements. In this paper in context of PM measurement traceability, we present systematically the (1) air quality regulation in different countries, (2) reference methods for size and mass measurements, (3) variation/error and limitations of PM measurements based on the current results in this study and previously published results, (4) current status of PM size and mass calibration facility, (5) expected uncertainty in PM measurements, (6) add-on uncertainty in other parameters of national ambient air quality standards due to PM measurements, (7) where does traceability of PM issue stand against other parameters of air quality standards and its impact on health and climate, (8) NMIs working on this issue, (9) status at Bureau International des Poids et Mesures (BIPM), France and (10) conclusion. The aim of this paper is to better understand the importance of international system of units (SI) traceability issue in PM measurements, so wherever and whenever it is measured, should be acceptable everywhere, and data should be comparable for improving air quality and thus the quality of life. Funding agencies should be aware of this issue, and accept the results from the principle investigators and team only when their results have the traceability link to SI. NMIs should make program to involve industries in gas and aerosol metrology work to fulfill the requirement of calibration and standards. The regulatory authorities/ministry should work together with NMIs to improve the data quality of ambient measurements. This will greatly help to better make the policies and decisions on the related impacts. These were also the ultimate goals of “one-day pre-AdMet workshop” organized at National Physical Laboratory, New Delhi, India on February 20th, 2013.

Thinking about Quoins: Strategic Traceability of Architecturally Significant Requirements

Architectural degradation is a common problem in most nontrivial, long-lived software systems. By identifying architecturally significant requirements and establishing trace links from those requirements, via architectural decisions, to code, we can keep developers informed of underlying architectural decisions and help preserve code quality during change maintenance. The Web extra at http://youtu.be/U6MAlOvJpQY is an audio podcast of author Jane Cleland-Huang reading her Requirements column, in which she discusses how requirements engineers can keep developers informed of underlying architectural decisions and help preserve code quality during change maintenance.

Performance evaluation of polycyclic aromatic hydrocarbons analysis in sediment from proficiency testing with metrological reference values

This paper describes a metrological approach to evaluate the measurement capability of laboratories participating in two proficiency testing (PT) programmes involving the analysis of five polycyclic aromatic hydrocarbons (PAHs) in sediment samples. Reference values of PAHs in the programmes for performance assessment were obtained from an accurate isotope dilution gas chromatography mass spectrometry (ID-GCMS) method which was thoroughly validated and verified. Isotope dilution mass spectrometry (IDMS) technique usually has a well-defined measurement uncertainty budget and a traceability link to an International System of Units. Provision of the metrological reference values in PT enables the establishment of a technical platform to assess the actual competence of the participating laboratories in sediment PAHs analysis. Results of the PT programmes showed that about 80% of the laboratories employed gas chromatography in their analyses and the remaining used liquid chromatography. Irrespective of the techniques being used, however, the majority of the participating laboratories were observed to underestimate values in which the mean values of the five reported PAHs were less than those of the ID-GCMS-derived reference values by 13-20%. Only 41-44% of the participating laboratories were able to achieve satisfactory z-scores. The present study revealed that the reinforcement of the capability for accurate measurement of PAHs in sediment samples in laboratories worldwide should be addressed.

Ensuring Domain Conformity by Means of the Topological Functioning Model

Abstract The paper focuses on an open question about ensuring conformity among a domain of system knowledge, an analysis domain, a software design domain, and code. Principles of Model Driven Engineering are used in the research, namely, formal specifications of domains and support of conformity among these specifications with the help of traceability mechanism. Topological Functioning Model (TFM) has a mathematical mechanism for supporting traceability, and, thus, conformity. The main results demonstrate that the TFM holds the central place in a chain of trace links between specifications, and analysis of these links enables verification of domain conformity. The results are theoretical and require additional practical experiments.

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.

Trustrace: Mining Software Repositories to Improve the Accuracy of Requirement Traceability Links

Traceability is the only means to ensure that the source code of a system is consistent with its requirements and that all and only the specified requirements have been implemented by developers. During software maintenance and evolution, requirement traceability links become obsolete because developers do not/cannot devote effort to updating them. Yet, recovering these traceability links later is a daunting and costly task for developers. Consequently, the literature has proposed methods, techniques, and tools to recover these traceability links semi-automatically or automatically. Among the proposed techniques, the literature showed that information retrieval (IR) techniques can automatically recover traceability links between free-text requirements and source code. However, IR techniques lack accuracy (precision and recall). In this paper, we show that mining software repositories and combining mined results with IR techniques can improve the accuracy (precision and recall) of IR techniques and we propose Trustrace, a trust--based traceability recovery approach. We apply Trustrace on four medium-size open-source systems to compare the accuracy of its traceability links with those recovered using state-of-the-art IR techniques from the literature, based on the Vector Space Model and Jensen-Shannon model. The results of Trustrace are up to 22.7 percent more precise and have 7.66 percent better recall values than those of the other techniques, on average. We thus show that mining software repositories and combining the mined data with existing results from IR techniques improves the precision and recall of requirement traceability links.

Facilitating design communication through engineering information traceability

AbstractTraceability of information provides the basis for assessing the credibility of engineering information, better understanding it, and making judgments about the appropriateness of its use for a particular design task. The presented research attempts to answer how the proposed traceability methodology and framework could help designers to improve communication, eventually create new channels of communication, and contribute to the creation of shared understanding in collaborative design processes. The discussion of these issues is based on a literature review, empirical research, observations of industrial practice, and feedback from initial implementation. The research is focused on information objects (IOs), archetypically represented in the engineering domain as technical documents that are often complex structures constituted of textual, numerical, and graphical fragments. The presented approach is based on an abstraction of IOs' relationships organized around specific contexts that are defined by a subset of product development ontology. Each IO could be repeatedly represented in various contexts that may contain different subsets of objects and their relationships. Such a representation also acts as a container in which the ontology concept instances are associated with IOs being developed and traced during the design episode. The usage of the proposed traceability methodology is discussed with examples of implementation and possible utilization situations. The paper is focused on explaining how the developed functionalities could help to resolve manifestations of inadequate information flow, which cause communication barriers in engineering companies. In addition, the proposed traceability methodology offers the possibility to record the detailed history of actions and events associated with IOs in the usage process of the product life cycle management systems. Based on the research findings, this paper argues that such a network of the traceability links and relationships may be viewed as a novel design communication channel.

Tracing Links Between Business Processes and Software Systems

The traceability links existing between a business process and the supporting software systems s is a critical concern for the organizations, as it directly affects their performance. Methodologies and tools are needed for detecting these kinds of relationships and keeping an evidence of the existing connections. This paper proposes an approach for modelling a business processes evidencing the links existing between their activities and the components of the supporting software systems. The approach described in this paper is concerned with the use of information retrieval techniques to software maintenance and, in particular, to the problem of recovering traceability links between the business process models and the components of the supporting software system. An information retrieval approach is introduced based on two processing phases including syntactic and semantic analysis. The application of the approach is explored through a case study.

Unique identification of elements in evolving software models

Evolving models are often managed in file-based software configuration management systems. This causes the identification problem: if the model elements are not assigned with globally unique identifiers, we cannot identify them over time. However, if such identifiers would be given, they can be misleading because the elements to which they are assigned might change completely. As a consequence, evolution becomes incomprehensible, partial transformation is hampered, and sufficient management of inter-model relationships (e.g. traceability links) is impeded. This article presents an approach to identify model elements or even complete model fragments over time. It establishes a fine-grained history representation to describe model evolution. The representation contains identification links between the elements of different model revisions allowing us to identify elements of a given revision in other revisions or variants of the model. Due to the explicit expression of model evolution, it further enables the capturing of changes that have been applied to the fine-grained elements inside a model.