Extracting Business Rules Research Articles

A Black-Box Computational Business Rules Extraction Approach through Test-Driven Development

Business rules extraction is an important activity in situations in which a software system becomes obsolete and needs to be replaced by a newer system, since the replacing system needs to satisfy the business rules embedded in the legacy software system. In this paper, we investigate an approach in which the computational business rules of a legacy software system can be extracted given previously generated output of the system and without requiring access to the system’s source code. Furthermore, extracted computational business rules are validated automatically with minimal involvement of domain experts through Test-Driven Development (TDD) such that test cases are constructed from historic output of the system. The proposed approach is applied to extract the computational business rules of a large-scale governmental payroll legacy software system. The study results demonstrate that the suggested approach extracted computational business rules van meet a substantial number of test cases. Thus, the efforts involving domain experts can be reduces to analyze such instances.

Business rule extraction using decision tree machine learning techniques: A case study into smart returnable transport items

Decision support systems are becoming increasingly sophisticated (e.g., being machine learning-based), attempting to automate decisions as much as possible. However, it remains challenging to extract meaningful value from large quantities of data while also maintaining transparency in seeking justification for the choices made. Instead of creating methods for increasing the interpretability of black box models, one way forward is to design models that are inherently interpretable in the first place. Rule-based methods can automate decisions with great transparency and accuracy, helping to ensure compliance with regulations and adherence to organizational guidelines. In this paper, we propose an approach that uses a decision tree machine learning classification technique for extracting business rules from IoT-generated data to predict the asset status of Smart Returnable Transport Items (SRTIs). We report on an industrial case study that uses two years of historical data, obtained from an SRTI provider in the Netherlands, to predict the status of smart pallets. We compare the performance with the results obtained by using a support-vector machine (SVM) technique. Our experiments show that our solution is both accurate and flexible in terms of business rule elicitation. The obtained decision trees are human-interpretable, can easily be combined with other decision-making techniques, and provide a prediction accuracy marginally higher than an SVM technique.

Генерація багатоцільових формальних моделей із успадкованого коду

In this paper a method for generation of formal models from legacy systems code is proposed. The purpose of these models is to have a possibility of their application in different tasks such as automatic generation of executable tests, translation to modern programming languages, reverse engineering. The method pursues goals to decrease complexity of state space search and checking formulas satisfiability in relation to the direct code modeling, and to help legacy systems understanding and implementing in modern technologies. We focused on formalization of Cobol memory model as it is the most common language in legacy systems. Formal model is an attribute transition system with control flow. We proposed an algorithm for building enumerated types for any variables whose usage fulfills certain conditions, including translation procedure of numeric variables to enumerated ones. We considered a problem of translating non-comparable structures which overlap in memory (operator redefines in Cobol), are copied or compared with each other. In opposite to common approach of using union semantics (like union construction in C++), we described a method of structure fields decomposition which has no drawbacks of unions and makes for minimization of bytewise approach. We considered the developed method on the examples of structures as with simple fields as with arrays. We also gave examples of realization of bytewise approach in Java and C++ languages for those variables that cannot be represented as enumerated or numeric attributes. We tried this work for tests generation for middle-sized projects (up to 100 000 lines of code) where it showed efficiency of developed method, also generated formal models were used for debugging of Cobol to Java translator and business rules extraction.

Cross-Organisational Process Mining in Cloud Environments

Cloud computing market is continually growing in the last years and becoming a new opportunity for business for private and public organisations. The diffusion of multi-tenants distributed systems accessible by clouds leads to the birth of some cross-organisational environments, increasing the organisation efficiency, promoting the business dynamism and reducing the costs. In spite of these advantages, this new business model drives the interest of researchers and practitioners through new critical issues. First of all, the multi-tenant distributed systems need new techniques to improve the traditional resource management distribution along the different tenants. Secondly, new approaches to the process analysis and monitoring analysed since cross-organisational environments allow various organisations to execute the same process in different variants. Hence, information about how each process variant characterised can be collected by the system and stored as process logs. The usefulness of such logs is twofold: these logs can be analysed using some process mining techniques to understand and improve the business processes and can be used to find better resource management and scalability. This paper proposes a cloud computing multi-tenancy architecture to support cross-organisational process executions and improve resource management distribution. Moreover, the approach supports the systematic extraction/composition of distributed data from the system event logs that are assumed to carry information of each process variant. To this aim, the approach also integrates an online process mining technique for the runtime extraction of business rules from event logs. Declarative processes are used to represent process variants running on the analysed infrastructure as they are particularly suited to represent the business process in a context characterised by low predictability and high variability. In this work, we also present a case study where the proposed architecture is implemented and applied to the execution of a real-life process of online products selling.

Two-tiered Clustering Classification Experiments for Market Segmentation of EFTPOS Retailers

This paper proposes the application of clustering and classification techniques on finding groupings of retailers who use the Electronic Funds Transfer at Point Of Sale (EFTPOS) facilities of a major bank in Australia. The RFM (Recency, Frequency, Monetary) analysis on each retailer is used to reduce the large data set of customer purchases through the EFTPOS network for the purpose of the retailer clustering. We then incorporate attributes of the EFTPOS transaction data in addition to the derived RFM attributes to build a decision tree to facilitate the extraction of business rules that explain the characteristics of the retailer clusters.

Mining business rules from business process model repositories

Purpose – Business process has become the core assets of many organizations and it becomes increasing common for most medium to large organizations to have collections of hundreds or even thousands of business process models. The purpose of this paper is to explore an alternative dimension to process mining in which the objective is to extract process constraints (or business rules) as opposed to business process models. It also focusses on an alternative data set – process models as opposed to process instances (i.e. event logs). Design/methodology/approach – The authors present a new method of knowledge discovery to find business activity sequential patterns embedded in process model repositories. The extracted sequential patterns are considered as business rules. Findings – The authors find significant knowledge hidden in business processes model repositories. The hidden knowledge is considered as business rules. The business rules extracted from process models are significant and valid sequential correlations among business activities belonging to a particular organization. Such business rules represent business constraints that have been encoded in business process models. Experimental results have indicated the effectiveness and accuracy of the approach in extracting business rules from repositories of business process models. Social implications – This research will assist organizations to extract business rules from their existing business process models. The discovered business rules are very important for any organization, where rules can be used to help organizations better achieve goals, remove obstacles to market growth, reduce costly mistakes, improve communication, comply with legal requirements, and increase customer loyalty. Originality/value – There has very been little work in mining business process models as opposed to an increasing number of very large collections of business process models. This work has filled this gap with the focus on extracting business rules.

Getting Objects Methods and Interactions by Extracting Business Rules from Legacy Systems

The maintenance of legacy systems becomes over the years extremely complex and highly expensive due to the incessant changes of company activities and policies. In this case, a new or an improved system must replace the previous one. However, replacing those systems completely from scratch is also very expensive and it represents a huge risk. The optimal scenario is evolving those systems by profiting from the valuable knowledge embedded in them. This paper aims to present an approach for knowledge acquisition from existing legacy systems by extracting business rules from source code. In fact, the business rules are extracted and assigned next to the domain entities in order to generate objects methods and interactions in an object-oriented platform. Furthermore, a rules translation in natural language is given. The aim is advancing a solution for re-engineering legacy systems, minimize the cost of their modernization and keep very small the gap between the company business and the renovated systems.

Business Rule Management Framework for Enterprise Web Services

Making a business rule extraction more dynamic is an open issue, and we think it is feasible if we decompose the business process structure in a set of rules, each of them representing a transition of the business process. As a consequence the business process engine can be realized by reusing and integrating an existing Rule Engine. We are proposing a way for extracting the business rules and then to modify it at the runtime. Business rules specifies the constraints that affect the behaviors and also specifies the derivation of conditions that affect the execution flow. The rules can be extracted from use cases, specifications or system code. But since not many enterprises capture their business rules in a structured, explicit form like documents or implicit software codes, they need to be identified first, before being captured and managed. These rules change more often than the processes themselves, but changing and managing business rules is a complex task beyond the abilities of most business analysts. The capturing process focuses on the identification of the potential business rules sources. As business logic requirements change, business analysts can update the business logic without enlisting the aid of the IT staff. The new logic is immediately available to all client applications. In current trend the rules are modified or changed in the static time phase. But this paper provides to change the rules at the run time. Here the rules are extracted from the services and can be a changed dynamically. The existing rules are modified and attached to source code without hindering service to the end user which can be achieved with source control systems. When the rules are revised, it provides a path in budding new business logic. This new business logic can be adopted for the efficient software development.

Highlighting Document's Structure

Abstract — In this paper, we present symbolic recognition models to extract knowledge characterized by document structures. Focussing on the extraction and the meticulous exploitation of the semantic structure of documents, we obtain a meaningful contextual tagging corresponding to different unit types (title, chapter, section, enumeration, etc.). Keywords — Information retrieval, document structures, symbolic grammars. I. I NTRODUCTION OCUMENT management (classification, indexation, storage) is practised for a long time, in particular in libraries. In recent years, due to technological progress and the transition from paper to numerical form, the amount of the textual resources has become gigantic [1], making it difficult to exploit manually. In order to make a better use of this large resource, information retrieval (henceforth IR) systems were created, aiming at improving the quality and efficiency of knowledge extraction. Our project objective is to produce the prototype of a system that would extract business rules from corporate texts and translate them into a visual software engineering model. Within this perspective, it is not only a question of extracting knowledge, but also to be able to visualize correctly extracted relevant information. The upstream of this project, object of this paper, is to extract knowledge characterized by document structure and contents. We will focus here on the extraction and exploitation of the semantic structure of documents in order to obtain a meaningful contextual tagging corresponding to different unit types (title, chapter, section, enumeration, etc.). This paper is organized as follow. First, we will underline previous researches in information retrieval that have taken into account document structure to improve results. Then, we will describe the models we propose for the detection of some

A method for the manual extraction of business rules from legacy source code

One of the main barriers to the successful migration of legacy systems is lack of information about the system to be migrated.With the complexity of modern information systems, it is rarely the case that an organisation has a complete end-to-endunderstanding of its systems, business processes and information. This is particularly true of the business rules enforced by suchsystems, which are often poorly documented (if at all) and incompletely understood by those who own, use and maintain suchsystems. These business rules must be migrated, along with the other system functionality, but this is difficult when they areburied deep within the source code of the system. In this paper, we propose a method for the discovery of business rules throughinspection of the source code of legacy systems. We also describe the results of a comprehensive evaluation exercise undertakenwithin BT, in which the method was applied to a legacy system about to undergo migration. The results of this study indicatedconsiderable success for the method in extracting valid business rules, but also highlighted some weaknesses. Most significantly,the cost of applying the method is a factor of the size and complexity of the system being analysed, and not of the number ofbusiness rules elicited. In order to address this, we propose a cut-down version of the method that can be used to produce a quickestimate of the likely yield of business rules from a program. Together, these two forms of the method aid personnel engaged insystem migration projects in making maximum use of the scarce resources are available for business rule discovery.

Extracting Business Rules from Information Systems

Business rules are an important asset of an organisation. Many organisations have substantial investment in business rules encoded in legacy information systems. This paper reports on the type of business rules that may be found in an organisation's information systems, and the techniques that may be used to recover business rules from such systems. Also outlined is a new approach which may be used to unlock business rules from legacy information systems.

Business rule extraction techniques for COBOL programs

Business rules are operational rules, often coded into software, that business organizations follow to perform various activities, such as transaction processing, quality control, business planning and database management. Over time, business rules evolve and the software that implemented them are also changed and maintained. As the encompassing software becomes large and aged, the business rules embedded are difficult to extract and understand, evolving with the encompassing software over time. Furthermore, the encompassing software is changed without changing the corresponding text documents, and thus, often, the business organization trusts the code more than any other documents. This paper proposes several techniques to extract business rules from legacy code. It is possible to use a generic software maintenance tool to extract business rules; however this can be an expensive exercise. We propose a tailored solution approach for the business rule extraction (BRE) problem, which combines variable classifications, program slicing, heuristics for identifying slicing criteria, multiple representations of business rules, bottom-up data flow analysis and hierarchical abstraction, among other maintenance techniques. The proposed solution approach has been implemented as a system and successfully tried with a number of industrial programs. © 1998 John Wiley & Sons, Ltd.