In system development, reuse of modules from existing systems is an important issue. However, such reuse becomes difficult in similar but customized applications because the relations among module functions are complicated. To address this problem, we aim to establish a technology that can extract, from design documents, a knowledge graph that represents the functional structure of a system and use it in development of other systems. In this paper, we introduce our knowledge-graph extraction framework and describe an experiment show a partial extraction.
Nowadays, software systems are rarely developed from scratch. Instead, most reuse existing modules. Rational Derivational Development and Software Product Line (SPL) development are typical styles of system development using extant modules. In doing such development, it is necessary to consider the functions and relations among available modules (e.g. their behaviors and functionality) along with their purposes, conditions, and requirements. However, migrating more complicated modules increases the difficulty for developers to acquire knowledge about the system functions because they have to read many documents, find the descriptions of functions of interest, and summarize them.
This research aims to support developers seeking to understand the functional
configuration of systems, particularly those developed with reuse of several existing
modules, by using a knowledge graph to represent how the system and its functions work.
The goal is to simplify development of a common platform for a series of system
products. We establish technology that can extract the knowledge graphs of system
functions (function graphs) from design documents and integrate the graphs into a
comprehensive model as a conceptual platform (Fig. 1) [
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Derivational Developments for Customers …
This paper introduces a part of our trial approach for extracting a knowledge graph of system functions from design documents. In Section 2, we explain the extraction method. In Section 3, we describe an experiment in which expressions are detected in functions and, in Section 4, we discuss the result. In Section 5, we summarize this paper. 2
In this research, a function graph is a directed graph network in which nodes and links (edges) correspond functions and their relations, respectively. An example of a function graph is shown in Fig.2.
The nodes contain partial text from documents describing functions, such as the names of the functions or the process in which the functions are used. Three types of relations between functions are defined. Same-as: texts indicate the same function (Super-)Sub: one function includes the other as a sub-function (Pre-)Post: one function is executed after the other begins
A function graph is extracted from design documents semi-automatically by supervised learning model. An overview of the extraction is shown in Fig.3. First, texts in the document are analyzed and divided into phrases, which typically consist of several words. The phrases are converted into numerical vectors according to their words, meanings as defined in a domain ontology, and modifying relations. After that, each vector is classified into a class of expression for some function, or not. Finally, pairs of function phrases are classified into a class in which some type of relation (including a null relation) exists between the pair. To prepare the learning data, the system developers checked and corrected the results of classification and updated the learning data in an online fashion before each classification. Moreover, the developers updated the ontology for feature extension.
The phrases and their pairs are converted into feature vectors based on documents. However, developers determine which phrases are treated as functions and what relations are indicated by the phrases. Therefore, a function graph represents the developers’ interpretation and understanding of the design documents.
1. Extraction Function Model Documents Learning
Data －－－－－ －－－－－ －－－－－ －－－－－ －－－－－ －－－－－ Detection of expression of
function 3. Adding Knowledge • Resolve spelling variants • Generalization of concepts
Domain Ontology
Creation of relations among functions
Integration 2. Checking and Correcting • Correcting errors • Complementing for missing data
Developer
Feature Models [
A point-of-sales (POS) system in the retail domain is a representative product, composed of several modules and often customized for each retailer. Design documents issued by the Open-POS Technical Council (OPOS document) are available for POS systems with various specifications. Using the OPOS document, we performed an experiment to detect expressions of functions.
First, we selected 18 chapters from the OPOS document, treating these as smaller documents for designing each of the system modules: a line display, a scanner, a coin dispenser, and so on. From one chapter, we manually built a function graph. Then, we created and updated classification models with the learning data, applied the model to another module document, extracting that document’s function graph, checked and corrected it, and finally added learning data and updated the models. We repeated this sequence for different orders of presentation of the modules.
In this paper, we introduced a prospective approach for extracting a functional model, which represents the functional structure of a target system, from system design documents. This should improve the reusability of development aspects. To show the working of this approach, we described an experiment in which expressions of functions were detected from documents. In future work, we intend to develop an entire framework for extracting and using functional graphs, increase the application’s generality, and evaluate it.