Developers include analysts, designers, programmers, and quality assurance staff. Managers include product managers, project managers, and quality managers.
The people who participate in problem and solution specification may play many of these roles, but they always share a common need. All participants need to share a common understanding, i. The initial stages of problem specification usually involve the various stakeholders and a requirements analyst. Each stakeholder has a viewpoint and opinions regarding the nature of the problem s to be solved and the needs to be satisfied by the software solution.
All these sometimes disparate viewpoints need inclusion, reconciliation and representation in the problem specification. The requirements analyst often serves as a mediator among the stakeholders to establish consensus regarding needs and priorities. Then, the requirements analyst inevitably serves as a translator between the stakeholders and the software developers, who often speak very different languages.
Stakeholders speak in terms of business needs, goals and perceivable functionality. Software developers speak in terms of models, design and programming, size, effort, and schedule.
It is a responsibility of the requirements analyst to understand the limitations of modeling formalisms and natural language.
The requirements analyst can help stakeholders reshape their thinking to achieve their desired outcomes, often by rephrasing problems and reframing 1 the contexts within which those problems are embedded. Requirements analysts need to be able to explore alternatives for modeling, but stakeholders must validate that the analytic conceptual models match their mental models.
So, we still need natural language for the humane expression of those models. Problems and requirements begin with natural language. For the benefit of often non-technical stakeholders, they must also finally be expressed with natural language, even though they must also be analyzed and formalized for translation into software. The importance of effective communication between all the participants involved with information systems cannot be overstated.
The quality of the communication between people determines their commonality 2 — the degree to which they share mental models of domain problems and solution usage requirements Figure 1. Commonality ultimately determines the quality of the resulting software solution — the degree to which the software solves the domain problems and satisfies the usage requirements.
Commonality supports software quality by insuring that the people involved in specifying a problem and those involved in solving the problem share a common understanding of the problem. Without commonality, the likelihood of developing a satisfactory and usable software solution is drastically reduced.
So, stakeholders and developers need to share overall and detailed mental models of the relevant problem elements, issues and rules, as well as the objectives and goals that need to be satisfied by a software solution.
The kinds of linguistic analysis discussed in this paper are based on the traditional word categories found in English grammar. We can rephrase sentences so that they are meaningful on multiple levels and to multiple audiences, including the various kinds of stakeholders and software developers.
With suitable constraints on syntax, natural language can serve as a rich source for natural conceptual models. Natural conceptual models retain much of the character of the natural language from which they were derived. Natural conceptual models can help people share and compare their mental models, and thereby help them establish commonality with respect to a particular problem domain.
Conceptual models in general are especially valuable for large systems they help manage complexity , for systems that involve medium to large teams, and for projects and products that endure for several years they help a business transfer knowledge and train staff. Linguistic analysis and conceptual models can also dramatically ease the process of object-oriented analysis. Conceptual models provide a preliminary vocabulary and relational models for the elements of a problem domain.
In turn, these models ease the task of object-oriented analysis — the analysis work becomes more one of organization and selection rather than raw object discovery. Also, conceptual models provide a link from the object-oriented analysis models back to the source material found in the original problem descriptions and solution usage requirements. This link directly supports greater traceability — from code back through design and analysis models, back through conceptual models to the problem descriptions and usage requirements.
Software development methodologies SDMs typically begin only after a problem has been articulated Figure 2. Each SDM prescribes a relatively mechanical process for transforming requirements into code. So, SDMs tend to be solution oriented rather than problem oriented. While SDMs may recommend the documentation of problems and requirements, they seldom provide any guidance regarding how to describe a problem in the first place.
In this regard, object-oriented methods are no better than structured methods. SDMs usually presume that a problem to be solved and the uses of a software solution have been well articulated.
Waterfall SDMs further presume that the requirements will not change substantially between the time they were collected and the completion of system development. However, many years of industry experience have shown both these presumptions to be flawed in both theory and practice.
Even when a problem has been relatively well articulated, usage requirements on a software solution often change, especially in business domains where products and services must quickly respond to customer demands and competitive market pressures. If anything, these factors accentuate the need for requirements elicitation, analysis and management. Requirements provide the baseline against which software developers can measure their solution to determine whether it solves the problems and satisfies the needs of stakeholders.
Substantially as a result of these factors, requirements management has become an important area of practice for software development. Requirements management provides the basis for making a software process repeatable.
So, it serves as the foundation on which the remainder of the CMM rests. Any methodology that does not explicitly address requirements and that does not include requirements management is fundamentally incomplete.
It does not address the real problem of software development — how do we engineer customer satisfaction? At least part of the answer must be to build our models using their language — i. So, natural conceptual models can serve as a good starting point for requirements management — because natural conceptual models add formality over natural language while retaining proximity to their natural language origins.
In the past, the utilization of natural language elements to influence software design has been largely tacit and informal. Smalltalk was derived from earlier programming languages, like Simula, 6 that focused on objects, especially objects that were independent of particular programs i.
The extensible keyword syntax of Smalltalk directly supports the modeling of natural language expressions, especially those languages which support a subject — verb — object SVO syntax order. An example of this capability will be provided later in this paper. In spite of these limitations, his explorations raised the possibility that software theory and quantitative analysis might extend to natural language, and they suggested the existence of a mapping from natural language to computational primitives operands and operators.
Thereafter, Booch 11,12 extended this approach into an explicitly object-oriented design process. Common nouns suggest data types i. Proper nouns and references suggest objects. Verbs, attributes, predicates, and descriptive expressions suggest operators. Control structures are suggested by English phrases using if, then, else, for, do, until, when, etc. He concluded the following:. The concepts used to understand a problem originally are generally the best concepts to use to write the program to solve the problem.
This is not to say that the first idea one has is necessarily the best approach to take. Nevertheless, it is usually a good idea to identify and formalize the intuitive concepts, that is, data types, with which the program is concerned.
This paper also supports the use of natural language as the conceptual basis for software development. Retaining as much as possible of the syntax and semantics of natural language problem descriptions in our software models has clear advantages for explaining those models and for transferring to others the original domain knowledge upon which they were based. In , Saeki, Horai, and Enomoto 13 proposed a software design process based on natural language.
Their work elaborates upon and complements the heuristics offered by Abbott. They focused particularly on the identification of dynamic system behavior as expressed by the verbs in a natural language description.
Their work offers many useful ideas regarding the information needed to represent the relationships that exist between natural language elements and some rules for selecting message senders and receivers. However, care must be exercised, as without appropriate balance, a focus on verbs can skew the orientation of software designs away from objects toward processes and functions. In , Carasik, Johnson, Patterson, and Von Glahn 14 exposed the limitations of using entity — relationship models to define semantic intensions, and they argued persuasively for a unified view of meaning and for the usage of conceptual modeling languages and knowledge representation techniques to represent meaning.
They argued that the division of the world into noun entity , verb relationship , and adjective value traditionally used by entity — relationship models is not helpful for the formal representation of meaning, and that these distinctions are not significant for conceptual models. However, while conceptual equivalents often exist between some noun, verb and descriptive adjective, the selection of nouns, verbs or adjectives to articulate domain semantics is often not arbitrary to stakeholders.
While purified conceptual models may be ideal for knowledge representation, reducing conceptual models to basic roles and case relations between concepts makes them too arcane for general use by stakeholders. So, while entity — relationship models may not finally be adequate to the task of domain description, conceptual models need not diverge so far from natural language as to be unintelligible to stakeholders.
In , Cockburn 15 investigated the application of linguistic metaphors to object-oriented design. While his investigation does not supply one, a linguistic model for such transformations between the primary word classes is rather straightforward.
This paper offers such a model and suggests that adverbs and adjectives should not merely be reduced to their corresponding verbs.
They need to be considered part of a larger framework — adverbs, adjectives and even verbs may also be reduced to nouns. We also explore the use of language as a metaphorical basis analogical source for the structure syntax of objects and object messages, and for the naming semantics of software components — class names, object references, variable names and method names. Again in , Cordes and Carver 16 introduced one of the first attempts to apply automated tools to requirements analysis and the automatic generation of object models from requirements documents.
While the translation of the initial requirements into a suitable knowledge base requires human interaction to resolve ambiguities, the subsequent translation of the domain knowledge into object models is automated. Cordes and Carver acknowledged that the translation of formalized knowledge into object models is sensitive to the quality of the initial requirements specification as one would expect. Still, their process and tools can help a requirements analyst begin to bridge the gap between informal requirements and formal software models.
The prospect of automating more of the analysis process is certainly intriguing. While the tools are still under development, the project goals are encouraging.
Over the past 25 years, we can observe that the object-oriented approach has been progressively extended to cover programming, design and analysis. This tradition and trend continues with the extension of the object-oriented approach into the realms of requirements elicitation, domain description and modeling.
The primary goal of this work is to make natural language suitable for conceptual modeling. It depends on the type of application that is needed. A simple GUI and website to establish a presence for a company can be generated for non-developers. In fact, many website tools are now available that does not require coding. When GPT-3 is applied, it can make things even simpler by allowing the user to describe what they want and then they can customize it later.
Users can simply use a tool to that generates the code and builds the application. For the most part, these are non-commercial applications. For advanced users i.
This can help game developers build virtual worlds by simply describing it while the tool renders the output. A smartphone app developer can quickly build modules by describing it while the tool generates it. What this does is save time for the developers, allowing them to complete tasks much faster than before. That helps boost productivity as it gives more time for developers to focus on the project and not stress about deadlines. GPT3 is a really powerful tool. Having billion parameters gives it so many possible perspectives that is available from the entire store of knowledge on the Internet.
These vast sources of data allows the model to use the parameters for network calculations that gives it the ability to learn from simple input, and generate complex output. In some examples of GPT-3, not related to software development, users have been able to get an entire story written through AI.
GPT-3 is like tapping into a brain that stores the best knowledge and information. It uses the parameters to calculate weights and probabilities to come up with answers to what a user requests.
The GPT-3 model uses these sources to learn and create knowledge. It is much like how a student goes to school to study and learn computer programming. With persistence and determination, an ordinary human can learn to code and build an application in a few weeks to months.
I think that you can use GPT-3 to accelerate software development and human learning as well. You not only generate the code, but it also helps the developer learn and improve their own education that can only lead to better applications. For example, a developer could use GPT-3 to help build better and more efficient code that results in improved application performance.
After seeing the demos, it is not a farfetched idea that you can generate an application by simply describing it in simple human language. No more need to code for hours at a time, but will this have an effect on how people function in the real world? Critics might say that this can lead to laziness and a total reliance on technology. Such jargons are typically organized into interfaces faces between parts that are named groups of messages.
Recent advances in programming models directly support this kind of organization. The Java programming language includes syntax for defining and implementing interfaces. In the realm of domain analysis, the use of facets little faces has emerged as an important technique for classifying the features of software components Figure 5.
Facets reflect different aspects of the software components they describe. They can reflect the viewpoints of different stakeholders, or different roles that a component may play in various relationships.
Users observe and manipulate software object models through such views. The metaphors used to design the "look and feel" of these displayed surfaces have evolved over several years.
Recently, such surface designs have begun to evolve from merely graphical user interfaces GUIs to truly object-oriented user interfaces OOUIs. Object-oriented user interfaces support direct manipulation of the underlying software objects via appropriate and intuitive metaphors and affordances presented on display surfaces.
Interfaces, facets and surfaces all serve as faces for software objects. Each face has its own language appropriate for discussion, analysis and design. However, the underlying elements of these languages all have a common basis in the metaphors offered by natural language. Given the metaphorical similarities between software object communications and human communications, it seems natural to organize software object systems similar to the ways in which human groups are organized.
In fact, it has been shown that the best object-oriented software designs often result from a responsibility-driven approach. The fitness of the assignments of responsibilities depends on a number of factors primarily related to the localization of data and functions close to their use.
Recognition of the importance of these design optimizations led to the development of the Law of Demeter. The Law of Demeter supports coupling control, narrow interfaces, information hiding, restriction and localization. So, the Law of Demeter serves as a useful heuristic for deciding how best to distribute responsibilities in an object system. Because several qualities are desirable in object-oriented designs, the responsibility expressed by a verb may span multiple objects in the final system design.
Responsibility may be partly distributed over several collaborating objects in a system, or it may be further decomposed within a given object class. Each related method signature may have greater or fewer arguments depending on the computational needs of each method. So, very general action verbs may need to be decomposed into their constituent actions in relation to the objects that undergo changes. The assignment of responsibilities to an object depends on the role s it plays in a system.
Roles serve as an increasingly important metaphor for communicating object-oriented software designs, and recognition of their importance has grown in recent years. The codification of object-oriented software design knowledge in Design Patterns 23 is founded in part on the metaphor of roles.
Software design patterns describe reusable collaborations between design elements. Each design element plays an identifiable role with well defined responsibilities.
Networked and independent processes communicate with each other through message exchanges. So, the metaphors for designing communicating processes are analogous to those used to design interacting objects, especially if the processes are themselves composed of objects.
This has led to the development of distributed object systems, extending the software object metaphors beyond the scope of a single process and across the communities of computers interlinked throughout the world. As a result, we are seeing the emergence of software agents that serve as human representatives in virtual communities. As we evolve these agents, they are becoming interactive, autonomous and self-replicating viral , and useful for searching, querying, accessing, filtering, and reporting from networked information resources.
There are also efforts to make these software agents exhibit a kind of intelligence by supplying them with encoded knowledge in formalized ontologies. The knowledge encoded by such ontologies embodies conceptual relationships, purified semantic information that is primarily linguistic in origin.
Over two decades of industry experience has shown the benefits of building software systems with objects, including rapid deployment, easier maintenance, and substantial reuse of both code and design. Software objects provide a natural and convenient way to organize and model systems in software. The object-oriented approach creates significant opportunities for building software models of high quality and conceptual integrity. The elements of the best software object system designs closely resemble those of the domain they model, including their structure, organization, and behavior.
There are also some limitations in the object-oriented approach. Today's commercial object-oriented programming languages still limit object designs to the embodiment of noun and verb phrases. If used at all, the other word classes are only used in naming conventions for classes, instances and methods. Unfortunately, the limitations of programming languages have profoundly influenced most analysis and design methodologies. However, the limitations in the expressiveness of object-oriented programming languages need not limit how we analyze problems and requirements.
The consideration of only nouns and verbs in previous analytic approaches is incomplete. Limiting our analytic considerations to only nouns and verbs prematurely limits and artificially impoverishes the conceptual models we build, which in turn has direct consequences upon the software systems we build.
The modeling languages used for software development tend to be elaborate and complex e. The primary reason for this is that models constructed with these languages need to include a level of detail sufficient to specify the design of a software system. While simplified versions of these languages may be used during analysis, these languages tend to focus on solution modeling rather than problem modeling.
These kinds of models are appropriate for software developers, but they are not especially suitable for stakeholders who are not generally trained in their construction and interpretation. The languages used to model conceptual structures e. Conceptual structures are appropriate for machines and computational linguists. They provide a mechanism for representing knowledge and the semantics of natural language expressions. They provide a level of formality suitable for manipulation by computers - i.
While conceptual structures are derived from natural language, they are arcane and unintelligible to lay people i. Stakeholders need a modeling language that provides some degree of formality, while remaining very simple and close to natural language. These motivations led to the development of a natural conceptual modeling language NCML to support natural conceptual models. Natural conceptual models have both a graphical form and a textual form.
However, this paper focuses on the textual form. Even with suitable modeling tools, building and maintaining graphical models can be time consuming - i. This is not to say that graphical models are not valuable. They are, but a balanced approach to their use is recommended e. For an introduction to the graphical language and a complete example of its use, please refer to the related paper available on the Web.
People have twisted minds and formulate complex thoughts. At least, they often express their thoughts with complex sentences. This complexity makes human communication more efficient by reducing or eliminating redundant phrases. Such eloquence is certainly appropriate for ordinary rhetoric. However, to make sentences suitable for modeling, we need to greatly simplify their syntax, often at the expense of some apparent redundancy - e.
There are several transformations that can be applied to sentences that preserve their overall meaning while producing simple and consistent syntactic formats. Collectively, we can characterize these transformations as syntactic normalization. Simplifying the syntax of sentences makes them less prone to ambiguity.
Such simplicity can also help people to share and compare their mental models. Thus, the additional formality supports commonality. Sometimes a complex sentence cannot be simplified to the desired degree without first exposing the meaning of some of its constituent phrases. In these situations, semantic exploration may provide clues for recovering nouns or verbs from descriptive adjectives and adverbs, and other kinds of phrases.
Normalized sentences are simple declarative sentences, sometimes called kernel sentences or nuclear sentences. The following definition extends that presented by Carasik, et.
The following table shows some illustrative examples of normalized sentences. Each example is preceded by the number of actants required by the example verb. The verbs listed are typical, including examples of both transitive and intransitive verbs. The initial examples are prototypical - i. The remaining examples identify appropriate prepositions, and the actants name thematic semantic roles that are appropriately associated with the example verb.
The following brief example was excerpted from a larger one. The full example describes the operations of a hazardous chemical storage depot regulated by the Environmental Protection Agency EPA.
The key problem associated with its operations is the allocation of storage resources i. Each storage building is licensed to hold a maximum number of drums. The regulation states that a depot is vulnerable if any two neighboring buildings contain the maximum number of drums.
The first sentence describes the depot buildings. Several applications of syntactic normalization are needed to simplify the sentence and split it into its constituent clauses. The second sentence describes the licensing of the storage buildings.
A mix of syntactic normalization and semantic exploration is needed to reveal the several ideas contained in the sentence. Likewise, the sentences in the second paragraph need a mixture of normalization and exploration. Candidate domain elements are indicated with bold. Try it out and let us know your feedback and ideas in the Power Apps community forum. By signing up, you agree to the terms of service.
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