20 citations · 121 across the 24 of their papers we have counts for
27 papers
On the Impact of Requirement Smells in LLM-Based Code Generation
Hugo Villamizar, Jannik Fischbach, Mert Şahin +5
Software requirements are typically incorporated into prompts used in LLM-assisted software development. Recent work has shown that requirement smells can affect automated traceabi…
Replications, Revisions, and Reanalyses: Managing Empirical Evidence in Software Engineering
Julian Frattini, Jannik Fischbach, Davide Fucci +4
One aspired outcome of empirical research on quantitative data is a variance theory, i.e., a quantification of the effect of an independent on a dependent variables. The validity o…
Human-AI Collaboration in Requirements Engineering: Evidence of the Negative Effect of LLMs on Requirements Inspection
Giovanna Broccia, Julian Frattini, Chetan Arora +4
Background. Requirements inspection (RI) is a well-established practice for detecting potential defects in requirements artifacts early in the software lifecycle. Recent advances i…
Information is all you need: Requirements Engineering Quality Reframed
Henning Femmer, Julian Frattini
To move beyond this vague appeal to context, this vision proposes a novel holistic theory of requirements engineering (RE) quality. This theory models how information particles, i.…
Replications, Revisions, and Reanalyses: Managing Variance Theories in Software Engineering
Julian Frattini, Jannik Fischbach, Davide Fucci +2
Variance theories quantify the variance that one or more independent variables cause in a dependent variable. In software engineering (SE), variance theories are used to quantify -…
Crossover Designs in Software Engineering Experiments: Review of the State of Analysis
Julian Frattini, Davide Fucci, Sira Vegas
Experimentation is an essential method for causal inference in any empirical discipline. Crossover-design experiments are common in Software Engineering (SE) research. In these, su…
Requirements Quality Research Artifacts: Recovery, Analysis, and Management Guideline
Julian Frattini, Lloyd Montgomery, Davide Fucci +3
Requirements quality research, which is dedicated to assessing and improving the quality of requirements specifications, is dependent on research artifacts like data sets (containi…
Measuring the Fitness-for-Purpose of Requirements: An initial Model of Activities and Attributes
Julian Frattini, Jannik Fischbach, Davide Fucci +2
Requirements engineering aims to fulfill a purpose, i.e., inform subsequent software development activities about stakeholders' needs and constraints that must be met by the system…
NLP4RE Tools: Classification, Overview, and Management
Julian Frattini, Michael Unterkalmsteiner, Davide Fucci +1
Tools constitute an essential contribution to natural language processing for requirements engineering (NLP4RE) research. They are executable instruments that make research usable…
A Second Look at the Impact of Passive Voice Requirements on Domain Modeling: Bayesian Reanalysis of an Experiment
Julian Frattini, Davide Fucci, Richard Torkar +1
The quality of requirements specifications may impact subsequent, dependent software engineering (SE) activities. However, empirical evidence of this impact remains scarce and too…
Applying Bayesian Data Analysis for Causal Inference about Requirements Quality: A Controlled Experiment
Julian Frattini, Davide Fucci, Richard Torkar +4
It is commonly accepted that the quality of requirements specifications impacts subsequent software engineering activities. However, we still lack empirical evidence to support org…
Automatic extraction of cause-effect-relations from requirements artifacts
Julian Frattini, Maximilian Junker, Michael Unterkalmsteiner +1
Background: The detection and extraction of causality from natural language sentences have shown great potential in various fields of application. The field of requirements enginee…
CiRA: An Open-Source Python Package for Automated Generation of Test Case Descriptions from Natural Language Requirements
Julian Frattini, Jannik Fischbach, Andreas Bauer
Deriving acceptance tests from high-level, natural language requirements that achieve full coverage is a major manual challenge at the interface between requirements engineering an…
Requirements Quality Research: a harmonized Theory, Evaluation, and Roadmap
Julian Frattini, Lloyd Montgomery, Jannik Fischbach +3
High-quality requirements minimize the risk of propagating defects to later stages of the software development life cycle. Achieving a sufficient level of quality is a major goal o…
Let's Stop Building at the Feet of Giants: Recovering unavailable Requirements Quality Artifacts
Julian Frattini, Lloyd Montgomery, Davide Fucci +3
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 art…
Automatic ESG Assessment of Companies by Mining and Evaluating Media Coverage Data: NLP Approach and Tool
Jannik Fischbach, Max Adam, Victor Dzhagatspanyan +4
Context: Sustainable corporate behavior is increasingly valued by society and impacts corporate reputation and customer trust. Hence, companies regularly publish sustainability rep…
An initial Theory to Understand and Manage Requirements Engineering Debt in Practice
Julian Frattini, Davide Fucci, Daniel Mendez +5
Context: Advances in technical debt research demonstrate the benefits of applying the financial debt metaphor to support decision-making in software development activities. Althoug…
A Live Extensible Ontology of Quality Factors for Textual Requirements
Julian Frattini, Lloyd Montgomery, Jannik Fischbach +3
Quality factors like passive voice or sentence length are commonly used in research and practice to evaluate the quality of natural language requirements since they indicate defect…
Automatic Creation of Acceptance Tests by Extracting Conditionals from Requirements: NLP Approach and Case Study
Jannik Fischbach, Julian Frattini, Andreas Vogelsang +8
Acceptance testing is crucial to determine whether a system fulfills end-user requirements. However, the creation of acceptance tests is a laborious task entailing two major challe…
Causality in Requirements Artifacts: Prevalence, Detection, and Impact
Julian Frattini, Jannik Fischbach, Daniel Mendez +3
Background: Causal relations in natural language (NL) requirements convey strong, semantic information. Automatically extracting such causal information enables multiple use cases,…
How Do Practitioners Interpret Conditionals in Requirements?
Jannik Fischbach, Julian Frattini, Daniel Mendez +3
Context: Conditional statements like "If A and B then C" are core elements for describing software requirements. However, there are many ways to express such conditionals in natura…
Transfer Learning for Mining Feature Requests and Bug Reports from Tweets and App Store Reviews
Pablo Restrepo Henao, Jannik Fischbach, Dominik Spies +2
Identifying feature requests and bug reports in user comments holds great potential for development teams. However, automated mining of RE-related information from social media and…
CATE: CAusality Tree Extractor from Natural Language Requirements
Noah Jadallah, Jannik Fischbach, Julian Frattini +1
Causal relations (If A, then B) are prevalent in requirements artifacts. Automatically extracting causal relations from requirements holds great potential for various RE activities…
Fine-Grained Causality Extraction From Natural Language Requirements Using Recursive Neural Tensor Networks
Jannik Fischbach, Tobias Springer, Julian Frattini +3
[Context:] Causal relations (e.g., If A, then B) are prevalent in functional requirements. For various applications of AI4RE, e.g., the automatic derivation of suitable test cases…