Abstract
Social interactions , shaping technology, allows us to investigate how technology pervades work practices , hence, understanding communities of practice with respect to technology. On the one hand, social shaping of technology highlights risk perception of technological evolution . On the other hand, technological evolution is a potential hazard, disruptive, for work practices . However, it is possible to analyse and capture technology trajectories in order to understand, with respect to technological evolution, design decisions and activities enabling engineering knowledge growth . The review of different case studies uncovers multidisciplinary aspects of technology innovation . It highlights complex interactions affecting our ability to support technology evolution , hence, technology innovation .
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Notes
- 1.
Software engineering processes, for instance, involve the activities of software specification, design, implementation, validation and evolution [42] . Process models (e.g. waterfall, spiral) organise and relate phases to each other differently (according to underlying assumptions and application domain constraints).
- 2.
The acquisition, deployment and use of COTS systems is somehow problematic due to limited accounts of, and guidance for assessing, human factors [12] .
- 3.
It is necessary to enrich the semantics interpretation of the accessibility relation, i.e. dependency, between functional requirements by associating weights with each pair of related possible worlds, i.e. functional requirements . Intuitively, technical solutions (matching requirements ) are accessible possibilities or possible worlds in solution spaces available in the production environment. A solution space, therefore, is just a collection of solutions, which represent the organisational engineering knowledge [44] resulting from the social shaping of technology [30] . The definition in [15] intentionally recalls the notion of possible worlds underlying Kripke models . Thus, solutions are Kripke models , whereas problems are formulas of (propositional) modal logic. Collections of problems (i.e. problem spaces) are suspected issues arising during system production. Kripke models (i.e. solutions) provide the semantics in order to interpret the validity of (propositional) modalities (i.e. problems). Propositional Modal Logic allows us to express modalities . Formulas of propositional modal logic capture system properties like safety and liveness . For instance, let us consider the formula \({\square}\,P\,{\rightarrow}\,P\). The formula means that if a property \(P\) is valid at every accessible possible world, then it is actually valid at the real world. It represents a simple safety property that states ‘nothing bad ever happens’. Another example is the formula \({\square}\,P \,{\rightarrow}\, {\diamondsuit}\, P\). The formula means that if the property P is valid at every accessible possible world, then it will be valid eventually. It represents a simple liveness property that states ‘something good eventually happens’.
- 4.
Hazard and Operability Analysis (HAZOP) [26, 43] involves “identifying the interconnections between components within the system and determining the corresponding interactions” [43] . Component interactions identify flows, referred as “entities” having certain properties or “attributes”, which define the system’s operation. Any deviation from these properties (i.e. attributes) highlights concerns with respect to the system’s operation [26, 43] .
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Anderson, S., Felici, M. (2012). Technological Evolution . In: Emerging Technological Risk. Springer, London. https://doi.org/10.1007/978-1-4471-2143-5_3
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