Business rule mining is the process of extracting essential intellectual business logic in the form of business rules from packaged or legacy software applications, recasting them in natural or formal language, and storing them in a source rule repository for further analysis or forward engineering. The goal is to capture these legacy business rules in a way that the business can validate, control and change them over time.
Business rule mining supports a Business rules approach, which is defined as a formal way of managing and automating an organization's business rules so that the business behaves and evolves as its leaders intend.
It is also commonly conducted as part of an application modernization project evolving legacy software applications to service-oriented architecture (SOA) solutions, transitioning to packaged software, redeveloping new in-house applications, or to facilitate knowledge retention and communication between business and IT professionals in a maintenance environment.
Alternative approaches to rule mining are manual and automated.
A manual approach involves the hand-writing of rules on the basis of subject matter expert interviews and the inspection of source code, job flows, data structures and observed behavior.
Manually extracting rules is complicated by the difficulty of locating and understanding highly interdependent logic that has been interwoven into millions of lines of software code.
An automated approach utilizes repository-based software to locate logical connections inherent within applications and extract them into a predetermined business rules format.
With automation, an effective approach is to apply semantic structures to existing applications. By overlaying business contexts onto legacy applications, rules miners can focus effort on discovering rules from systems that are valuable to the business. Effort is redirected away from mining commoditized or irrelevant applications.
Further, best practices [1] coupled with various tool-assisted techniques of capturing programs’ semantics speeds the transformation of technical rules to true business rules. Adding business semantics to the analysis process allows users to abstract technical concepts and descriptors that are normal in an application to a business level that is consumable by a rules analyst.
System integrators, software vendors, rules mining practitioners, and in-house development teams have developed technologies, proprietary methodologies and industry-specific templates for application modernization and business rule mining.
In artificial intelligence, an expert system is a computer system emulating the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural code. The first expert systems were created in the 1970s and then proliferated in the 1980s. Expert systems were among the first truly successful forms of artificial intelligence (AI) software. An expert system is divided into two subsystems: the inference engine and the knowledge base. The knowledge base represents facts and rules. The inference engine applies the rules to the known facts to deduce new facts. Inference engines can also include explanation and debugging abilities.
Knowledge representation and reasoning is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can utilize to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge in order to design formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of reasoning, such as the application of rules or the relations of sets and subsets.
In computing, a legacy system is an old method, technology, computer system, or application program, "of, relating to, or being a previous or outdated computer system," yet still in use. Often referencing a system as "legacy" means that it paved the way for the standards that would follow it. This can also imply that the system is out of date or in need of replacement.
Static program analysis is the analysis of computer software that is performed without actually executing programs, in contrast with dynamic analysis, which is analysis performed on programs while they are executing. In most cases the analysis is performed on some version of the source code, and in the other cases, some form of the object code.
In computer science and information science, an ontology encompasses a representation, formal naming and definition of the categories, properties and relations between the concepts, data and entities that substantiate one, many, or all domains of discourse. More simply, an ontology is a way of showing the properties of a subject area and how they are related, by defining a set of concepts and categories that represent the subject.
In computer science, specifically software engineering and hardware engineering, formal methods are a particular kind of mathematically rigorous techniques for the specification, development and verification of software and hardware systems. The use of formal methods for software and hardware design is motivated by the expectation that, as in other engineering disciplines, performing appropriate mathematical analysis can contribute to the reliability and robustness of a design.
Computer science is the study of the theoretical foundations of information and computation and their implementation and application in computer systems. One well known subject classification system for computer science is the ACM Computing Classification System devised by the Association for Computing Machinery.
A business rule defines or constrains some aspect of business and always resolves to either true or false. It specifically involves terms, facts and rules. Business rules are intended to assert business structure or to control or influence the behavior of the business. Business rules describe the operations, definitions and constraints that apply to an organization. Business rules can apply to people, processes, corporate behavior and computing systems in an organization, and are put in place to help the organization achieve its goals. Even though a system of strategic processes govern business rules, business rules are not strategic themselves; they are directive.
In software testing, test automation is the use of software separate from the software being tested to control the execution of tests and the comparison of actual outcomes with predicted outcomes. Test automation can automate some repetitive but necessary tasks in a formalized testing process already in place, or perform additional testing that would be difficult to do manually. Test automation is critical for continuous delivery and continuous testing.
A BRMS or business rule management system is a software system used to define, deploy, execute, monitor and maintain the variety and complexity of decision logic that is used by operational systems within an organization or enterprise. This logic, also referred to as business rules, includes policies, requirements, and conditional statements that are used to determine the tactical actions that take place in applications and systems.
Automated code review software checks source code for compliance with a predefined set of rules or best practices. The use of analytical methods to inspect and review source code to detect bugs or security issues has been a standard development practice in both Open Source and commercial software domains. This process can be accomplished both manually and in an automated fashion. With automation, software tools provide assistance with the code review and inspection process. The review program or tool typically displays a list of warnings. A review program can also provide an automated or a programmer-assisted way to correct the issues found. This is a component for mastering easily software. This is contributing to the Software Intelligence practice. This process is usually called "linting" since one of the first tools for static code analysis was called Lint.
Legacy modernization, also known as software modernization or platform modernization, refers to the conversion, rewriting or porting of a legacy system to modern computer programming languages, architectures, software libraries, protocols or hardware platforms. Legacy transformation aims to retain and extend the value of the legacy investment through migration to new platforms to benefit from the advantage of the new technologies.
Frames are an artificial intelligence data structure used to divide knowledge into substructures by representing "stereotyped situations". They were proposed by Marvin Minsky in his 1974 article "A Framework for Representing Knowledge". Frames are the primary data structure used in artificial intelligence frame language; they are stored as ontologies of sets.
Knowledge Discovery Metamodel (KDM) is a publicly available specification from the Object Management Group (OMG). KDM is a common intermediate representation for existing software systems and their operating environments, that defines common metadata required for deep semantic integration of Application Lifecycle Management tools. KDM was designed as the OMG's foundation for software modernization, IT portfolio management and software assurance. KDM uses OMG's Meta-Object Facility to define an XMI interchange format between tools that work with existing software as well as an abstract interface (API) for the next-generation assurance and modernization tools. KDM standardizes existing approaches to knowledge discovery in software engineering artifacts, also known as software mining.
The Semantics of Business Vocabulary and Business Rules (SBVR) is an adopted standard of the Object Management Group (OMG) intended to be the basis for formal and detailed natural language declarative description of a complex entity, such as a business. SBVR is intended to formalize complex compliance rules, such as operational rules for an enterprise, security policy, standard compliance, or regulatory compliance rules. Such formal vocabularies and rules can be interpreted and used by computer systems. SBVR is an integral part of the OMG's model-driven architecture (MDA).
Business process management (BPM) is the discipline in which people use various methods to discover, model, analyze, measure, improve, optimize, and automate business processes. Any combination of methods used to manage a company's business processes is BPM. Processes can be structured and repeatable or unstructured and variable. Though not required, enabling technologies are often used with BPM.
Knowledge extraction is the creation of knowledge from structured and unstructured sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to information extraction (NLP) and ETL, the main criteria is that the extraction result goes beyond the creation of structured information or the transformation into a relational schema. It requires either the reuse of existing formal knowledge or the generation of a schema based on the source data.
In information technology a reasoning system is a software system that generates conclusions from available knowledge using logical techniques such as deduction and induction. Reasoning systems play an important role in the implementation of artificial intelligence and knowledge-based systems.
Continuous delivery (CD) is a software engineering approach in which teams produce software in short cycles, ensuring that the software can be reliably released at any time and, when releasing the software, without doing so manually. It aims at building, testing, and releasing software with greater speed and frequency. The approach helps reduce the cost, time, and risk of delivering changes by allowing for more incremental updates to applications in production. A straightforward and repeatable deployment process is important for continuous delivery.
The Knowledge Based Software Assistant (KBSA) was a research program funded by the United States Air Force. The goal of the program was to apply concepts from artificial intelligence to the problem of designing and implementing computer software. Software would be described by models in very high level languages and then transformation rules would transform the specification into efficient code. The air force hoped to be able to generate the software to control weapons systems and other command and control systems using this method. As software was becoming ever more critical to USAF weapons systems it was realized that improving the quality and productivity of the software development process could have significant benefits for the military, as well as for information technology in other major US industries.
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