Models are used extensively in software engineering - to facilitate requirements analysis, architectural design, and so on. Models are also central to recent approaches to AI, being the output of computationally intensive machine learning algorithms drawing on large datasets. In this short paper, we first suggest that developing machine learning applications, like other software initiatives, can benefit from familiar requirements engineering (RE) techniques such as goal- and agent-oriented RE, so that the resulting systems will address the needs and wants of end-users and other stakeholders. Then, we suggest that the two kinds of modeling are fundamentally complementary in that they pertain to the two sides of a cognitive cycle. We propose that an RE framework needs to address both sides of the cognitive cycle for all relevant actors, in order to be able to respond to the complex socio-technological realities of a data-rich world.
Models play a crucial role in requirements engineering (RE). They offer a simplified view of reality, facilitating specialized and focused lines of inquiry and analysis. Models are also central to recent approaches to AI, being the output of computationally intensive machine learning algorithms drawing on large datasets. Today, machine learning (ML) has become pervasive in software systems and already has far reaching impacts on everyday life. Having effective RE techniques that ensure technology systems meet humans needs and wants is therefore of crucial importance to system designers as well as to technology users and society at large.
In this paper, we first argue that ML models, despite being computationally generated, require considerable human conceptual effort and reasoning to achieve desired results, and can therefore benefit from familiar RE techniques based on conceptual modeling such as goal-oriented approaches, with some modifications and extensions.
We then note that, although the two kinds of models and modeling appear to be at odds with each
other, one focusing on empirical data while the other relies on domain experts and human knowledge,
the two are ultimately complementary. Drawing on the notion of a cognitive cycle from the study of
human cognition [
We argue that a framework that conceptually encompasses both kinds of modeling can offer the more comprehensive requirements analysis that is needed for today’s complex sociotechnical realities. We envision an agent-oriented requirements modeling framework based on an abstract “knowledgelevel” cognitive agent covering the full cognitive cycle, so that each side of the cycle can potentially be implemented at the physical level with a mix of human and machine intelligence and enactment.
In building software systems and applications, we are now routinely encountering two kinds of
models. Models have been core to the practice of software engineering for a long time. It has been said
that the critical skill of the software professional and computer scientist is the ability to work with
abstractions [
Models of a different type are equally pervasive, in the empirical sciences. Models in this context are derived from data collected from the world. Models aim to accurately capture the essence of reality as it exists, so as to be able to build theories and understand phenomena, and to predict the future.
In the past decade or so, this second kind of modeling has received a big boost from computational advances - a convergence of advances in machine learning algorithms, raw computing power, and network and mobile technologies. As modern life has gone digital, the massive amounts of data that is the digital footprint of business transactions, social interactions, and physical activities are being tapped to produce models that in turn reshape our lives.
Although ML-generated models aim to faithfully reflect what is in the “raw data”, they are
nevertheless artifacts, produced through the deliberate actions and decisions of data scientists, ML
engineers, and project sponsors. While the specific activities in an ML project may be substantially
different from those in a conventional software development project [
The recently developed GR4ML framework [
As in other types of system development, goal-oriented requirements modeling can facilitate making the connection between domain problem understanding and technology solution, clarifying and justifying the step by step progression from problem to solution, all the while avoiding building solutions that solve the wrong problem. Some initiatives may start top-down from business drivers, while others may emphasize bottom-up leveraging of existing assets, such as readily available data assets and reusable components. Ultimately the top-down and bottom-up elements must align for a successful project. These are supported by connections across the three submodels in GR4ML – the Business View, the Analytics/ML Design View, and the Data Prep View [16].
The goal-oriented approach of GR4ML can potentially be extended to an agent-oriented approach
along the lines of i* [
By analyzing the network of dependencies among actors, one could determine whether the actor is using appropriate data sources and other inputs, which may come with biases reflecting the strategic interests of actors along the data supply chain. As in i*, quality (softgoal) dependencies among actors can be identified and evaluated.
Catalogues of design techniques for achieving common recurring softgoals can be compiled to assist
data scientists and system designers, as done for non-functional requirements (NFR’s) in conventional
software engineering [
The actor abstraction in i* hides the details of internal operations of the actor. How an actor is able
to produce an ML-model can be revealed incrementally by treating that actor as composed of multiple
collaborating actors each responsible for some part of the ML process, e.g., data sourcing, cleaning,
feature engineering, algorithm selection, training, testing, tuning, etc. These responsibilities could be
treated as roles which may be performed by the same or different individuals (i* agents) [
In treating an ML model as an artifact to be produced in a development project, as we did in the preceding section, we have not given special consideration to the complementary nature of the two type of models. Both types are models in that they are representations of reality, but each is arrived at and used differently. ML modeling generates an abstraction of the world based on empirical data, whereas conventional requirements and software engineering models are abstractions used by human professionals mainly to produce software that act on the world when instantiated. From the viewpoint of creating an intelligent agent or an effective information system, it is clearly equally important that its models of the world be grounded in empirical data, and that its vision of the desired world as expressed in models can be realized through action.
By analogy to human cognition [
Conventional software constructed based on handcrafted models with knowledge elicited from domain experts (e.g., through interviews, surveys, etc.) would typically have a long lag time on the perception-to-model side. Today, with ML, many software systems are approaching near real-time cognitive cycles in sensing the environment and responding to changes, by adopting, for example, selfadaptive software architectures [11] or data-driven requirements engineering techniques [12].
RE frameworks today need to encompass the full cognitive cycle, at least at a conceptual level (regardless of the extent to which ML models or conceptual models are directly used in the automated systems), so that requirements for both sides of the cycle will be identified, analyzed, and used to guide development of a coherent system comparable to an intelligent cognitive agent.
We briefly envision such a full cognitive cycle RE framework in the next section.
As automated systems are ultimately situated within a human social setting, requirements modeling
must cover the overall sociotechnical system [
We envision an agent-oriented requirements modeling framework based on an abstract “knowledgelevel” cognitive agent [14] covering the full cognitive cycle, so that each side of the cycle can potentially be a mix of human and machine intelligence and enactment. Early requirements analysis can thus probe requirements about model accuracy, potential bias, concept drift without prior commitment to implementation technology.
i* was inspired by classical AI and multi-agent systems where actors are modelled as having ability to achieve goals for each other [14, 15]. To accommodate the newly powerful machine-learning variety of AI, new constructs such as the new types of dependencies suggested in Section 3 could be added so that i* actors will cover the entire cognitive cycle. The actors in the extended i* will be actors with learning ability, though not necessarily machine learning.
The abstract actors will be mapped to physical agents for a more implementation-oriented analysis taking into account the strengths and limitations of various classes of machines and humans with respect to their learning and other abilities.
We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC). Constructive comments from anonymous reviewers and from S. Nalchigar, Z. Babar, and A. Lapouchnian are gratefully acknowledged.
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