Tackling Collaboration Challenges in the Development of ML-Enabled Systems

Collaboration on complex development projects almost always presents challenges. For traditional software projects, these challenges are well known, and over the years a number of approaches to addressing them have evolved. But as machine learning (ML) becomes an essential component of more and more systems, it poses a new set of challenges to development teams. Chief among these challenges is getting data scientists (who employ an experimental approach to system model development) and software developers (who rely on the discipline imposed by software engineering principles) to work harmoniously.

In this SEI blog post, which is adapted from a recently published paper to which I contributed, I highlight the findings of a study on which I teamed up with colleagues Nadia Nahar (who led this work as part of her PhD studies at Carnegie Mellon University and Christian Kästner (also from Carnegie Mellon University) and Shurui Zhou (of the University of Toronto).The study sought to identify collaboration challenges common to the development of ML-enabled systems. Through interviews conducted with numerous individuals engaged in the development of ML-enabled systems, we sought to answer our primary research question: What are the collaboration points and corresponding challenges between data scientists and engineers? We also examined the effect of various development environments on these projects. Based on this analysis, we developed preliminary recommendations for addressing the collaboration challenges reported by our interviewees. Our findings and recommendations informed the aforementioned paper, Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process, which I’m proud to say received a Distinguished Paper Award at the 44th International Conference on Software Engineering (ICSE 2022).

Despite the attention ML-enabled systems have attracted—and the promise of these systems to exceed human-level cognition and spark great advances—moving a machine-learned model to a functional production system has proved very hard. The introduction of ML requires greater expertise and introduces more collaboration points when compared to traditional software development projects. While the engineering aspects of ML have received much attention, the adjacent human factors concerning the need for interdisciplinary collaboration have not.

The Current State of the Practice and Its Limits

Most software projects extend beyond the scope of a single developer, so collaboration is a must. Developers normally divide the work into various software system components, and team members work largely independently until all the system components are ready for integration. Consequently, the technical intersections of the software components themselves (that is, the component interfaces) largely determine the interaction and collaboration points among development team members.

Challenges to collaboration occur, however, when team members cannot easily and informally communicate or when the work requires interdisciplinary collaboration. Differences in experience, professional backgrounds, and expectations about the system can also pose challenges to effective collaboration in traditional top-down, modular development projects. To facilitate collaboration, communication, and negotiation around component interfaces, developers have adopted a range of strategies and often employ informal broadcast tools to keep everyone on the same page. Software lifecycle models, such as waterfall, spiral, and Agile, also help developers plan and design stable interfaces.

ML-enabled systems generally feature a foundation of traditional development into which ML component development is introduced. Developing and integrating these components into the larger system requires separating and coordinating data science and software engineering work to develop the learned models, negotiate the component interfaces, and plan for the system’s operation and evolution. The learned model could be a minor or major component of the overall system, and the system typically includes components for training and monitoring the model.

All of these steps mean that, compared to traditional systems, ML-enabled system development requires expertise in data science for model building and data management tasks. Software engineers not trained in data science who, nevertheless, take on model building tend to produce ineffective models. Conversely, data scientists tend to prefer to focus on modeling tasks to the exclusion of engineering work that might influence their models. The software engineering community has only recently begun to examine software engineering for ML-enabled systems, and much of this work has focused narrowly on matters such as testing models and ML algorithms, model deployment, and model fairness and robustness. Software engineering research on adopting a system-wide scope for ML-enabled systems has been limited.

Framing a Research Approach Around Real-World Experience in ML-Enabled System Development

Finding limited existing research on collaboration in ML-enabled system development, we adopted a qualitative strategy for our research based on four steps: (1) establishing scope and conducting a literature review, (2) interviewing professionals building ML-enabled systems, (3) triangulating interview findings with our literature review, and (4) validating findings with interviewees. Each of these steps is discussed below:

• Scoping and literature review: We examined the existing literature on software engineering for ML-enabled systems. In so doing, we coded sections of papers that either directly or implicitly addressed collaboration issues among team members with different skills or educational backgrounds. We analyzed the codes and derived the collaboration areas that informed our interview guidance.
• Interviews: We conducted interviews with 45 developers of ML-enabled systems from 28 different organizations that have only recently adopted ML (see Table 1 for participant demographics). We transcribed the interviews, and then we created visualizations of organizational structure and responsibilities to map challenges to collaboration points (see Figure 1 for sample visualizations). We further analyzed the visualizations to determine whether we could associate collaboration problems with specific organizational structures.
• Triangulation with literature: We connected interview data with related discussions identified in our literature review, along with potential solutions. Out of the 300 papers we read, we identified 61 as possibly relevant and coded them using our codebook.
• Validity check: After creating a full draft of our study, we provided it to our interviewees along with supplementary material and questions prompting them to check for correctness, areas of agreement and disagreement, and any insights gained from reading the study.