Let's Stop Building at the Feet of Giants: Recovering unavailable Requirements Quality Artifacts

Requirements quality literature abounds with publications presenting artifacts, such as data sets and tools. However, recent systematic studies show that more than 80% of these artifacts have become unavailable or were never made public, limiting reproducibility and reusability. In this work, we report on an attempt to recover those artifacts. To that end, we requested corresponding authors of unavailable artifacts to recover and disclose them according to open science principles. Our results, based on 19 answers from 35 authors (54% response rate), include an assessment of the availability of requirements quality artifacts and a breakdown of authors' reasons for their continued unavailability. Overall, we improved the availability of seven data sets and seven implementations

Assets in Software Engineering: What are they after all?

During the development and maintenance of software-intensive products or services, we depend on various assets. These assets are important to the feasibility of the project and influence product's final quality. However, despite their central role in the software development process, little thought is yet invested into what assets eventually are, often resulting in many terms and underlying concepts being mixed and used inconsistently. A precise terminology of assets and related concepts, such as asset degradation, are crucial for setting up a new generation of cost-effective software engineering practices. In this position paper, we critically reflect upon the resulting notion of assets in software engineering. As a starting point, we define the terminology and concepts of assets and extend the reasoning behind them. We explore assets' characteristics such as value and persistence. We discuss what asset degradation is, its various types and the implications that asset degradation might bring for the planning, realisation, and evolution of software-intensive products and services over time. With our work, we aspire to contribute to a more standardised definition of assets in software engineering and foster research endeavours and their practical dissemination in a common, more unified direction.Comment: Manuscript submitted to the Journal of Systems and Softwar

Automatic ESG Assessment of Companies by Mining and Evaluating Media Coverage Data: NLP Approach and Tool

Context: Sustainable corporate behavior is increasingly valued by society and impacts corporate reputation and customer trust. Hence, companies regularly publish sustainability reports to shed light on their impact on environmental, social, and governance (ESG) factors. Problem: Sustainability reports are written by companies themselves and are therefore considered a company-controlled source. Contrary, studies reveal that non-corporate channels (e.g., media coverage) represent the main driver for ESG transparency. However, analysing media coverage regarding ESG factors is challenging since (1) the amount of published news articles grows daily, (2) media coverage data does not necessarily deal with an ESG-relevant topic, meaning that it must be carefully filtered, and (3) the majority of media coverage data is unstructured. Research Goal: We aim to extract ESG-relevant information from textual media reactions automatically to calculate an ESG score for a given company. Our goal is to reduce the cost of ESG data collection and make ESG information available to the general public. Contribution: Our contributions are three-fold: First, we publish a corpus of 432,411 news headlines annotated as being environmental-, governance-, social-related, or ESG-irrelevant. Second, we present our tool-supported approach called ESG-Miner capable of analyzing and evaluating headlines on corporate ESG-performance automatically. Third, we demonstrate the feasibility of our approach in an experiment and apply the ESG-Miner on 3000 manually labeled headlines. Our approach processes 96.7 % of the headlines correctly and shows a great performance in detecting environmental-related headlines along with their correct sentiment. We encourage fellow researchers and practitioners to use the ESG-Miner at https://www.esg-miner.com

Towards good-enough Requirements Engineering : a theoretical Foundation for Requirements Quality

Context: Requirements Engineering (RE) research has established a common agreement on the impact that the quality of requirements has on subsequent software development activities and artifacts. Furthermore, empirical investigations suppose that RE quality defects tend to scale in cost for remediation when left unattended. This motivates the need for requirements quality assurance. Problem: This need has been met with requirements quality research, which abounds with publications proposing writing rules and guidelines that are meant to ensure requirements of high quality. However, recent studies have questioned the rigor and relevance of these publications, which would undermine the practical applicability of requirements quality research: requirements quality is a means to an end and serves a specific purpose (i.e., minimizing the emitted risk on downstream activities), but when this purpose is not met due to lack of a rigor and practical relevance, the approach to researching requirements quality needs to be rethought. Aim: The notion of good-enough requirements engineering constitutes a context-sensitive, activity-based perspective on requirements quality. In this thesis, we aim at both (1) understanding and (2) exploring possibilities of operationalizing this notion. Methods: We employ a mixed-methods approach to achieve our aim. We use theory adoption in order to provide a theoretical foundation for requirements quality research, conduct a survey to understand the level of theory adherence in the requirements quality literature, and perform subject-based classification to generate an overview of theory-related elements proposed in literature.  Results: Through theory adoption we derive a harmonized, activity-based requirements quality theory that frames requirements quality according to its impact on subsequent activities and hence ensures its relevance. The subsequent survey confirms that there is a lack of rigor and relevance in previous requirements quality publications, which likely explains the lack of adoption of the research in practice. The overview of quality factors in a subject-based classification is a first step to centralize requirements quality research for visibility and effective reuse. Conclusion: The notion of good-enough requirements engineering has the potential to re-focus requirements quality research on a more profound notion of rigor and relevance. In this thesis, we report on a first requirements quality theory. Through adherence to this requirements quality theory and contribution to the central repository of subject-based classification, the operationalization of the concept of good-enough requirements engineering can effectively support predicting the impact that requirements quality has on subsequent software development activities in the future

CEREC : Causality Extraction from Requirements Artifacts

The cause-effect recognition (CEREC) system provides an API for causality extraction tailored to the requirements engineering context. The library is written in Java and is released under the MIT open source license. In this paper, the underlying algorithm is described, and a demonstration of the active learning component for causality extraction is outlined. The results are promising and strengthen the confidence in exploring automation approaches for model-based testing. © 2020 IEEE.open access</p

Embracing the Silence

One major challenge in an online classroom is students' engagement. Despite university guidelines, teachers have experienced students' aversion against turning on their camera during a lecture and against speaking up at all. This results in a very unidirectional interaction in which teachers receive little direct feedback about students perception of the course, particularly of their struggles. Instead of tediously encouraging students to turn on their cameras, we chose to embrace the silence and accommodate the new way of participating in online courses

Towards good-enough Requirements Engineering : a theoretical Foundation for Requirements Quality

Context: Requirements Engineering (RE) research has established a common agreement on the impact that the quality of requirements has on subsequent software development activities and artifacts. Furthermore, empirical investigations suppose that RE quality defects tend to scale in cost for remediation when left unattended. This motivates the need for requirements quality assurance. Problem: This need has been met with requirements quality research, which abounds with publications proposing writing rules and guidelines that are meant to ensure requirements of high quality. However, recent studies have questioned the rigor and relevance of these publications, which would undermine the practical applicability of requirements quality research: requirements quality is a means to an end and serves a specific purpose (i.e., minimizing the emitted risk on downstream activities), but when this purpose is not met due to lack of a rigor and practical relevance, the approach to researching requirements quality needs to be rethought. Aim: The notion of good-enough requirements engineering constitutes a context-sensitive, activity-based perspective on requirements quality. In this thesis, we aim at both (1) understanding and (2) exploring possibilities of operationalizing this notion. Methods: We employ a mixed-methods approach to achieve our aim. We use theory adoption in order to provide a theoretical foundation for requirements quality research, conduct a survey to understand the level of theory adherence in the requirements quality literature, and perform subject-based classification to generate an overview of theory-related elements proposed in literature.  Results: Through theory adoption we derive a harmonized, activity-based requirements quality theory that frames requirements quality according to its impact on subsequent activities and hence ensures its relevance. The subsequent survey confirms that there is a lack of rigor and relevance in previous requirements quality publications, which likely explains the lack of adoption of the research in practice. The overview of quality factors in a subject-based classification is a first step to centralize requirements quality research for visibility and effective reuse. Conclusion: The notion of good-enough requirements engineering has the potential to re-focus requirements quality research on a more profound notion of rigor and relevance. In this thesis, we report on a first requirements quality theory. Through adherence to this requirements quality theory and contribution to the central repository of subject-based classification, the operationalization of the concept of good-enough requirements engineering can effectively support predicting the impact that requirements quality has on subsequent software development activities in the future

CiRA : A Tool for the Automatic Detection of Causal Relationships in Requirements Artifacts

Requirements often specify the expected system behavior by using causal relations (e.g., If A, then B). Automatically extracting these relations supports, among others, two prominent RE use cases: Automatic test case derivation and dependency detection between requirements. However, existing tools fail to extract causality from natural language with reasonable performance. In this paper, we present our tool CiRA (Causality detection in Requirements Artifacts), which represents a first step towards automatic causality extraction from requirements. We evaluate CiRA on a publicly available data set of 61 acceptance criteria (causal: 32; non-causal: 29) describing the functionality of the German Corona-Warn-App. We achieve a macro1 score of 83 %, which corroborates the feasibility of our approach. © 2021 CEUR-WS. All rights reserved.open access</p

CiRA : A Tool for the Automatic Detection of Causal Relationships in Requirements Artifacts

Requirements often specify the expected system behavior by using causal relations (e.g., If A, then B). Automatically extracting these relations supports, among others, two prominent RE use cases: Automatic test case derivation and dependency detection between requirements. However, existing tools fail to extract causality from natural language with reasonable performance. In this paper, we present our tool CiRA (Causality detection in Requirements Artifacts), which represents a first step towards automatic causality extraction from requirements. We evaluate CiRA on a publicly available data set of 61 acceptance criteria (causal: 32; non-causal: 29) describing the functionality of the German Corona-Warn-App. We achieve a macro1 score of 83 %, which corroborates the feasibility of our approach. © 2021 CEUR-WS. All rights reserved.open access</p

Asset Management in Software Engineering : What is it after all?

When developing and maintaining software-intensive products or services, we often depend on various "assets", denoting the inherent value to selected artefacts when carrying out development and maintenance activities. When exploring various areas in Software Engineering, such as Technical Debt and our work with industry partners, we soon realised that many terms and concepts are frequently intermixed and used inconsistently. Despite the central role of assets to software engineering, management, and evolution, little thoughts are yet invested into what assets eventually are. A clear terminology of "assets" and related concepts, such as "value" or "value degradation", just to name two, are crucial for setting up effective software engineering practices. As a starting point for our own work, we had to define the terminology and concepts, and extend the reasoning around the concepts. In this position paper, we critically reflect upon the resulting notion of Assets in Software Engineering. We explore various types of assets, their main characteristics, such as providing inherent value. We discuss various types of value degradation and the possible implications of this on the planning, realisation, and evolution of software-intensive products and services over time.