Research Initiatives

This page lists the high-level initiatives that I pursue. Each initiative is characterized by an overarching goal to which individual publications contribute to.

Good-Enough Requirements Engineering

Good-enough requirements engineering was the leitmotif during my doctoral studies. This initiative aims to provide software engineering pracitioners with decision support to answer the question "Are my requirements good enough?" In the scope of this initiative, we developed a theoretical foundation - the activity-based requirements quality theory (RQT) - and started contributing empirical evidence to the theory. Our goal is to collect evidence about the impact of requirements quality factors (e.g., the use of passive voice) on subsequent activities (e.g., implementing or testing).

Reading List

If interested in this initiative, I recommend starting to read related work about artifact quality. We drew heavy inspiration from the fantastic work of Dr. Florian Deissenboeck on source code maintainability and Dr. Henning Femmer, who translated this work into the domain of requirements engineering.

1. Deissenboeck, F., Wagner, S., Pizka, M., Teuchert, S., & Girard, J. F. (2007, October). An activity-based quality model for maintainability. In 2007 IEEE International Conference on Software Maintenance (pp. 184-193). IEEE. DOI: 10.1109/ICSM.2007.4362631.
2. Femmer, H., Mund, J., & Fernández, D. M. (2015, May). It's the activities, stupid! a new perspective on RE quality. In 2015 IEEE/ACM 2nd International Workshop on Requirements Engineering and Testing (pp. 13-19). IEEE. DOI: 10.1109/RET.2015.11.
3. Femmer, H., & Vogelsang, A. (2018). Requirements quality is quality in use. IEEE Software, 36(3), 83-91. DOI: 10.1109/MS.2018.110161823.

These three papers inspired us to further develop the notion of activity-based artifact quality into a set of theories that define what requirements quality actually is. Simply put, the quality of a requirements artifact depends on its impact on subsequent activities in which the artifact is used, i.e., how well the requirements artifact fulfills its purpose. We formulated this in the requirements quality theory (RQT) and established taxonomies for both the dependent variables (i.e., the properties of requirements artifacts) and the independent variables (i.e., the properties of affected activities) of this relationship.

1. Frattini, J., Montgomery, L., Fischbach, J., Mendez, D., Fucci, D., & Unterkalmsteiner, M. (2023). Requirements quality research: a harmonized theory, evaluation, and roadmap. Requirements Engineering, 28(4), 507-520. DOI: 10.1007/s00766-023-00405-y.
2. Frattini, J., Montgomery, L., Fischbach, J., Unterkalmsteiner, M., Mendez, D., & Fucci, D. (2022, August). A live extensible ontology of quality factors for textual requirements. In 2022 IEEE 30th International Requirements Engineering Conference (RE) (pp. 274-280). IEEE. DOI: 10.1109/RE54965.2022.00041.
3. Frattini, J., Fischbach, J., Fucci, D., Unterkalmsteiner, M., & Mendez, D. (2024, June). Measuring the Fitness-for-Purpose of Requirements: An initial Model of Activities and Attributes. In 2024 IEEE 32nd International Requirements Engineering Conference (RE). IEEE. DOI: 10.1109/RE59067.2024.00047.

Consider these three theories as the foundation of the activity-based paradigm of requirements quality. Empirical research subscribing to this paradigm effectively propel a scientific understanding of requirements quality. The following two publications demonstrate how this can look.

1. Femmer, H., Kučera, J., & Vetrò, A. (2014, September). On the impact of passive voice requirements on domain modelling. In Proceedings of the 8th ACM/IEEE international symposium on empirical software engineering and measurement (pp. 1-4). 10.1145/2652524.2652554.
2. Frattini, J., Fucci, D., Torkar, R., Montgomery, L., Unterkalmsteiner, M., Fischbach, J., & Mendez, D. (2024). Applying Bayesian Data Analysis for Causal Inference about Requirements Quality: A Replicated Experiment. Empirical Software Engineering. DOI: 10.1007/s10664-024-10582-1

We encourage both scrutinizing the theoretical foundation of requirements quality and also contributing further empirical studies subscribing to it. The initiative has further produced resources like the online requirements quality factor ontology that shall engaging with it.

Causality in Requirements Artifacts

In the early years of my Ph.D. I had the pleasure to support Dr. Jannik Fischbach in his doctoral studies about the use of causal sentences in requirements artifacts. In a series of empirical studies we were able to show that causal sentences are prevalent in requirements artifacts, their interpretation is challenging, but automatic tools can be effectively leveraged to automatically derive test cases from them.

Reading List

To review the studies of the CiRA initiative, I recommend the following list of publications:

1. Fischbach, J., Hauptmann, B., Konwitschny, L., Spies, D., & Vogelsang, A. (2020, August). Towards causality extraction from requirements. In 2020 IEEE 28th International Requirements Engineering Conference (RE) (pp. 388-393). IEEE. DOI: 10.1109/RE48521.2020.00053.
2. Frattini, J., Fischbach, J., Mendez, D., Unterkalmsteiner, M., Vogelsang, A., & Wnuk, K. (2023). Causality in requirements artifacts: prevalence, detection, and impact. Requirements Engineering, 28(1), 49-74. DOI: 10.1007/s00766-022-00371-x.
3. Fischbach, J., Frattini, J., Mendez, D., Unterkalmsteiner, M., Femmer, H., & Vogelsang, A. (2021). How do practitioners interpret conditionals in requirements?. In Product-Focused Software Process Improvement: 22nd International Conference, PROFES 2021, Turin, Italy, November 26, 2021, Proceedings 22 (pp. 85-102). Springer International Publishing. DOI: 10.1007/978-3-030-91452-3_6.
4. Fischbach, J., Frattini, J., Vogelsang, A., Mendez, D., Unterkalmsteiner, M., Wehrle, A., ... & Wiecher, C. (2023). Automatic creation of acceptance tests by extracting conditionals from requirements: NLP approach and case study. Journal of Systems and Software, 197, 111549. DOI: 10.1016/j.jss.2022.111549.

The initiative produced several additional publications as well as a fully interactive online demonstration.

Open Science

I subscribe to the open science principles and strife to make all of my research accessible and reusable. In the scope of this endeavor, my colleagues and I also try to support the software engineering research community with guidelines on how to improve adherence to open science principles.

1. Frattini, J., Montgomery, L., Fucci, D., Unterkalmsteiner, M., Mendez, D., & Fischbach, J. (2024). Requirements quality research artifacts: Recovery, analysis, and management guideline. Journal of Systems and Software. DOI: 10.1016/j.jss.2024.112120.

The Open Science Artefact Management Guideline summarizes our concise recommendations on how to collect, document, licence, archive, and share research artifacts.

Software Engineering Research Methodology

I have recently developed an interest in methods for empirical methods and data analysis in software engineering research. Particularly, I am interested in proper statistical causal inference using structural causal modeling, directed acyclic graphs, and Bayesian data analysis. An initial set of publications is composed of the following, but is planned to grow in the near future.

1. Frattini, J., Fucci, D., Torkar, R. & Mendez, D. (2024). A Second Look at the Impact of Passive Voice Requirements on Domain Modeling: Bayesian Reanalysis of an Experiment. 1st International Workshop on Methodological Issues with Empirical Studies in Software Engineering (WSESE2024). DOI: 10.1145/3643664.3648211.
2. Frattini, J., Fucci, D., and Vegas, S. (2024, October). Crossover Designs in Software Engineering Experiments: Review of the State of Analysis. In Proceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM '24). Association for Computing Machinery, New York, NY, USA, 482-488. DOI: 10.1145/3674805.3690754.