LabVIEW application with embedded Lua scripting for a laser based measuring machine

Published ArticleThis paper presents the work on the development of software for an industrial laser based measuring machine. The goal being not only for a working application, but also to optimise the development process and ease future maintenance of the software. LabVIEW with its graphical method of programming allows engineers to easily create large software applications to control industrial processes and machines. This software if not properly designed can lead to stability and maintenance problems. The experience gained from developing, maintaining and improving a LabVIEW application for a laser measuring machine, results in the integration of the Lua scripting language into LabVIEW. It is shown how the embedded Lua allows the LabVIEW software application for the machine to be structured for simpler development and maintenance

Methodical recommendation to "Basics of software engineering. Laboratory practice". Part 1

Software engineering is an engineering discipline that is concerned with all aspects of software production. Software engineering can be divided into sub-disciplines. Some of them are: - Software engineering management: The application of management activities – planning, coordinating, measuring, monitoring, controlling, and reporting – to ensure that the development and maintenance of software is systematic, disciplined, and quantified. Requirements engineering: The elicitation, analysis, specification, and validation of requirements for software

Methodical recommendation to "Basics of software engineering. Laboratory practice". Part 1

Software engineering is an engineering discipline that is concerned with all aspects of software production. Software engineering can be divided into sub-disciplines. Some of them are: - Software engineering management: The application of management activities – planning, coordinating, measuring, monitoring, controlling, and reporting – to ensure that the development and maintenance of software is systematic, disciplined, and quantified. Requirements engineering: The elicitation, analysis, specification, and validation of requirements for software

SIMPLIFIED READABILITY METRICS

This paper describes a new approach to measuring the complexity of software systems with considering their readability. Readability Metrics were first proposed by Chung and Yung 181 in 1990. Software industry uses software metrics to measure the complexity of software systems for software cost estimation, software development control, software assurance, software testing, and software maintenance [3], [71, [9], 151, [18]. Most of the software metrics measure the software complexity by one or more of the software attributes. We usually class@ the software attributes that software metrics use for measuring complexity into three categories: size, control flow, and data flow [5], f71. All the three categories concern with the physical activities of software development. Readability Metrics have been outstanding among the existing software complexity metrics for taking nonphysical software attributes, like readability, into considerations [8]. The applications of Readability Metrics are good in indicating the additional efforts required for less readable software systems, and help in keeping the software systems maintainable. However, the numerous metrics and the complicated formulas in the family usually make it tedious to apply Readability Metrics to large scale software systems. In this paper, we propose a simplified approach to Readability Metrics. We reduce the number of required measures and keep the considerations on software readability. We introduce our Readability model in a more formal way. The Readability Metrics preprocesses algorithm is developed with compilers front-end techniques. The experiment results show that this simplified approach has good predictive power in measuring software complexity with software readability, in addition to its ease of applying. The applications of Readability Metrics indicate the readability of software systems and help in keeping the source code readable and maintainable.Information Systems Working Papers Serie

Comparison Study and Review on Object-Oriented Metrics

The best elucidations to software development problems are regularly touted as object-oriented processes. The popularity of object-oriented design metrics is essential in software engineering for measuring the software complexity, estimating size, quality and project efforts. There are various approaches through which we can find the software cost estimation and predicates on various kinds of deliverable items. Object-oriented metrics assures to reduce cost and the maintenance effort by serving as early predictors to estimate software faults. Such an early quantification augments the quality of the final software. This paper reviews object-oriented metrics. A comparison table is maintained via which we can analyze the difference between all the object-oriented metrics effectively

A Model Based on Software Quality Factors which Predicts Maintainability

Computer scientists are continually attempting to improve software system development. Systems are developed in a top-down fashion for better modularity and understandability. Performance enhancements are implemented for more speed. One area in which a great deal of effort is being devoted is software maintenance. Brooks estimates that fifty percent of the development cost of a software system is for maintenance activities [BROF82]. Since a large portion of the effort of a system is devoted to maintenance, it is reasonable to assume that driving down maintenance costs would drive down the overall cost of the system. Measuring the complexity of a software system could aid in this attempt. By lowering the complexity of the system or of subsystems within the system, it may be possible to reduce the amount of maintenance necessary. Software quality metrics were developed to measure the complexity of software systems. This study relates the complexity of the system as measured by software metrics to the amount of maintenance necessary to that system. We have developed a model which uses several software quality metrics as parameters to predict maintenance activity