Introducing an Evaluation Method for Taxonomies

Background: Taxonomies are crucial for the development of a research field, as they play a major role in structuring a complex body of knowledge and help to classify processes, approaches, and solutions. While there is an increasing interest in taxonomies in the software engineering (SE) research field, we observe that SE taxonomies are rarely evaluated. Aim: To raise awareness and provide operational guidance on how to evaluate a taxonomy, this paper presents a three step evaluation method evaluating its structure, applicability, and purpose. Method: To show the feasibility and applicability of our approach, we provide a running example and additionally illustrate our approach to a practical case study in SE research. Results and Conclusion: Our method with operational guidance enables SE researchers to systematically evaluate and improve the quality of their taxonomies and support reviewers to systematically assess a taxonomy\u27s quality

Detecting Inconsistencies in Software Architecture Documentation Using Traceability Link Recovery

Documenting software architecture is important for a system’s success. Software architecture documentation (SAD) makes information about the system available and eases comprehensibility. There are different forms of SADs like natural language texts and formal models with different benefits and different purposes. However, there can be inconsistent information in different SADs for the same system. Inconsistent documentation then can cause flaws in development and maintenance. To tackle this, we present an approach for inconsistency detection in natural language SAD and formal architecture models. We make use of traceability link recovery (TLR) and extend an existing approach. We utilize the results from TLR to detect unmentioned (i.e., model elements without natural language documentation) and missing model elements (i.e., described but not modeled elements). In our evaluation, we measure how the adaptations on TLR affected its performance. Moreover, we evaluate the inconsistency detection. We use a benchmark with multiple open source projects and compare the results with existing and baseline approaches. For TLR, we achieve an excellent F1-score of 0.81, significantly outperforming the other approaches by at least 0.24. Our approach also achieves excellent results (accuracy: 0.93) for detecting unmentioned model elements and good results for detecting missing model elements (accuracy: 0.75). These results also significantly outperform competing baselines. Although we see room for improvements, the results show that detecting inconsistencies using TLR is promising

Establishing a Benchmark Dataset for Traceability Link Recovery between Software Architecture Documentation and Models

In research, evaluation plays a key role to assess the performance of an approach. When evaluating approaches, there is a wide range of possible types of studies that can be used, each with different properties. Benchmarks have the benefit that they establish clearly defined standards and baselines. However, when creating new benchmarks, researchers face various problems regarding the identification of potential data, its mining, as well as the creation of baselines. As a result, some research domains do not have any benchmarks at all. This is the case for traceability link recovery between software architecture documentation and software architecture models. In this paper, we create and describe an open-source benchmark dataset for this research domain. With this benchmark, we define a baseline with a simple approach based on information retrieval techniques. This way, we provide other researchers a way to evaluate and compare their approaches

Recovering Trace Links Between Software Documentation And Code

Introduction Software development involves creating various artifacts at different levels of abstraction and establishing relationships between them is essential. Traceability link recovery (TLR) automates this process, enhancing software quality by aiding tasks like maintenance and evolution. However, automating TLR is challenging due to semantic gaps resulting from different levels of abstraction. While automated TLR approaches exist for requirements and code, architecture documentation lacks tailored solutions, hindering the preservation of architecture knowledge and design decisions. Methods This paper presents our approach TransArC for TLR between architecture documentation and code, using componentbased architecture models as intermediate artifacts to bridge the semantic gap. We create transitive trace links by combining the existing approach ArDoCo for linking architecture documentation to models with our novel approach ArCoTL for linking architecture models to code. Results We evaluate our approaches with five open-source projects, comparing our results to baseline approaches. The model-to-code TLR approach achieves an average F1-score of 0.98, while the documentation-to-code TLR approach achieves a promising average F1-score of 0.82, significantly outperforming baselines. Conclusion Combining two specialized approaches with an intermediate artifact shows promise for bridging the semantic gap. In future research, we will explore further possibilities for such transitive approaches

Recovering Trace Links Between Software Documentation And Code

Introduction Software development involves creating various artifacts at different levels of abstraction and establishing relationships between them is essential. Traceability link recovery (TLR) automates this process, enhancing software quality by aiding tasks like maintenance and evolution. However, automating TLR is challenging due to semantic gaps resulting from different levels of abstraction. While automated TLR approaches exist for requirements and code, architecture documentation lacks tailored solutions, hindering the preservation of architecture knowledge and design decisions. Methods This paper presents our approach TransArC for TLR between architecture documentation and code, using componentbased architecture models as intermediate artifacts to bridge the semantic gap. We create transitive trace links by combining the existing approach ArDoCo for linking architecture documentation to models with our novel approach ArCoTL for linking architecture models to code. Results We evaluate our approaches with five open-source projects, comparing our results to baseline approaches. The model-to-code TLR approach achieves an average F1-score of 0.98, while the documentation-to-code TLR approach achieves a promising average F1-score of 0.82, significantly outperforming baselines. Conclusion Combining two specialized approaches with an intermediate artifact shows promise for bridging the semantic gap. In future research, we will explore further possibilities for such transitive approaches

The psychological impact of prolonged disorders of consciousness on caregivers:a systematic review of quantitative studies

Objective: Systematic review of the nature, frequency and severity of psychological experiences of people who have a close relationship with a person with a prolonged disorder of consciousness. Data sources: Cochrane Library, Web of Science, PsycINFO, PubMed, Embase®, MEDLINE®, Allied and Complementary Medicine™, were searched from inceptions until December 2016 with additional hand searching of reference lists of included articles. Review methods: Studies were included that used quantitative methodologies and psychological measures to investigate experiences. The PRISMA statement was followed with inclusion criteria set a priori. A data synthesis summarized psychological constructs studied. Results: A total of 18 studies (ranging between n = 16–487 participants) met the inclusion criteria with 15 of 18 studies focused on the primary caregiver. A total of 23 standardized psychological measures were identified to assess four primary psychological constructs: Loss and grief, psychological wellbeing changes, burden and use of coping strategies. Conclusions: Small sample sizes, limited variables and reliance on observational methods affected quality. Caregivers do find ways to manage independently, but some exhibit clinically significant psychological distress that does not change over time alone and may get worse

Introducing an Evaluation Method for Taxonomies

Background: Taxonomies are crucial for the development of a research field, as they play a major role in structuring a complex body of knowledge and help to classify processes, approaches, and solutions. While there is an increasing interest in taxonomies in the software engineering (SE) research field, we observe that SE taxonomies are rarely evaluated. Aim: To raise awareness and provide operational guidance on how to evaluate a taxonomy, this paper presents a three step evaluation method evaluating its structure, applicability, and purpose. Method: To show the feasibility and applicability of our approach, we provide a running example and additionally illustrate our approach to a practical case study in SE research. Results and Conclusion: Our method with operational guidance enables SE researchers to systematically evaluate and improve the quality of their taxonomies and support reviewers to systematically assess a taxonomy\u27s quality

ArDoCo/Core: v0.30.0

<h1>Commits since last release</h1> <ul> <li>988832ea Release 0.30.0</li> <li>838b9da6 Auto-Update Dependencies<ul> <li>Updated org.xerial:sqlite-jdbc:jar:3.43.2.2 to version 3.44.0.0</li> </ul> </li> <li>d6915d82 Dependency(deps): Bump org.fuchss:xml-object-mapper from 0.5.0 to 0.6.0</li> <li>6a6cdf71 Dependency(deps): Bump com.github.javaparser:javaparser-core</li> <li>c6f0533e Dependency(deps): Bump org.apache.maven.plugins:maven-javadoc-plugin</li> <li>1b813eba Dependency(deps): Bump org.apache.maven.plugins:maven-failsafe-plugin</li> </ul> <h2>What's Changed</h2> <ul> <li>Dependency(deps): Bump io.github.ardoco:docker from 0.17.0 to 0.18.0 by @dependabot in https://github.com/ArDoCo/Core/pull/293</li> <li>Dependency(deps): Bump org.apache.maven.plugins:maven-failsafe-plugin from 3.2.1 to 3.2.2 by @dependabot in https://github.com/ArDoCo/Core/pull/297</li> <li>Dependency(deps): Bump org.apache.maven.plugins:maven-javadoc-plugin from 3.6.0 to 3.6.2 by @dependabot in https://github.com/ArDoCo/Core/pull/296</li> <li>Dependency(deps): Bump com.github.javaparser:javaparser-core from 3.25.5 to 3.25.6 by @dependabot in https://github.com/ArDoCo/Core/pull/295</li> <li>Dependency(deps): Bump org.fuchss:xml-object-mapper from 0.5.0 to 0.6.0 by @dependabot in https://github.com/ArDoCo/Core/pull/294</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/ArDoCo/Core/compare/v0.29.0...v0.30.0</p&gt