Reusing and Composing Tests with Traits

International audienceSingle inheritance often forces developers to duplicate code and logic. This widely recognized situation affects both business code and tests. In a large and complex application whose classes implement many groups of methods (protocols), duplication may also follow the application's idiosyncrasies, making it difficult to specify, maintain, and reuse tests. The research questions we faced are (i) how can we reuse test specifications across and within complex inheritance hierarchies, especially in presence of orthogonal protocols; (ii) how can we test interface behavior in a modular way; (iii) how far can we reuse and parametrize composable tests. In this paper, we compose tests out of separately specified behavioral units of reuse —traits. We propose test traits, where: (i) specific test cases are composed from independent specifications; (ii) executable behavior specifications may be reused orthogonally to the class hierarchy under test; (iii) test fixtures are external to the test specifications, thus are easier to specialize. Traits have been successfully applied to test two large and critical class libraries in Pharo, a new Smalltalk dialect based on Squeak, but are applicable to other languages with traits

Development of a decision support system for decision-based part/fixture assignment and fixture flow control = Ukusungulwa kohlelo lokuxhaswa kwezinqumo mayelana nokwabiwa kwezingxenye ezakhiwayo kanye nokuhanjiswa kwazo.

Doctoral Degree. University of KwaZulu-Natal, Durban.ABSTRACT: An intense competition in a dynamic situation has increased the requirements that must be considered in the current manufacturing systems. Among those factors, fixtures are one of the major problematic components. The cost of fixture design and manufacture contributes to 10-20% of production costs. Manufacturing firms usually use traditional methods for part/fixture assignment works. These methods are highly resource consuming and cumbersome to enumerate the available fixtures and stabilise the number of fixtures required in a system. The aim of this study was to research and develop a Decision Support System (DSS), which was useful to perform a decision-based part/fixture assignment and fixture flow control during planned production periods. The DSS was designed to assist its users to reuse/adapt the retrieved fixtures or manufacture new fixtures depending upon the state of the retrieved fixtures and the similarities between the current and retrieved cases. This DSS combined Case-Based Reasoning (CBR), fuzzy set theory, the Analytic Hierarchy Process (AHP) and Discrete-Event Simulation (DES) techniques. The Artificial Intelligence (AI) component of the DSS immensely used a fuzzy CBR system combined with the fuzzy AHP and guiding rules from general domain knowledge. The fuzzy CBR was used to represent the uncertain and imprecise values of case attributes. The fuzzy AHP was applied to elicit domain knowledge from experts to prioritise case attributes. New part orders and training samples were represented as new and prior cases respectively using an Object-Oriented (OO) method for case retrieval and decision proposal. Popular fuzzy ranking and similarity measuring approaches were utilised in the case retrieval process. A DES model was implemented to analyse the performances of the proposed solutions by the fuzzy CBR subsystem. Three scenarios were generated by this subsystem as solution alternatives that were the proposed numbers of fixtures. The performances of these scenarios were evaluated using the DES model and the best alternative was identified. The novelty of this study employed the combination of fuzzy CBR and DES methods since such kinds of combinations have not been addressed yet. A numerical example was illustrated to present the soundness of the proposed methodological approach. Keywords: Decision support systems, case-based reasoning, analytic hierarchy process, fuzzy set theory, object-oriented methods, discrete-event simulation, fixtures. IQOQA LOCWANINGO : Ukuncintisana okunezinhlelo eziguquguqukayo kulesi sikhathi samanje sekwenze ukuthi kube nezidingo ezintsha ezinhlelweni zokukhiqiza. Phakathi kwakho konke lokhu izingxenye (fixtures) zingezinye zezinto ezidala izinkinga. Intengo yokwakha uhlaka lwengxenye kanye nokuyikhiqiza kubiza amaphesenti ayi-10 kuya kwangama-20 entengo yokukhiqiza. Amafemu akhiqizayo avamise ukusebenzisa izindlela ezindala zomsebenzi wokwaba izingxenye. Lezi zindlela zidla kakhulu izinsizangqangi futhi kuthatha isikhathi eside ukubala izingxenye ezikhona nokuqinisekisa ukuthi kunesibalo esanele kulokho okumele kube yikho ohlelweni lokusebenza. Inhloso yalolu cwaningo bekungukucwaninga nokusungula i-Decision Support System (DSS) ebe lusizo ekwenzeni umsebenzi wokuthatha izinqumo ngokwabiwa kwezingxenye kanye nokuhanjiswa kwazo ngezikhathi ezimiselwe ukukhiqiza. I-DSS yakhelwa ukusiza labo abayisebenzisayo ukuze basebenzise noma bazisebenzise lapho zingakaze zisetshenziswe khona lezo zingxenye ezibuyisiwe, noma kwakhiwe ezintsha kuya ngokuthi zibuyiswe zinjani lezi ezibuyisiwe nokuthi ziyafana yini nalezo ezintsha. I-DSS isebenzise amasu ahlanganise i-Case-Based Reasoning (CBR), injulalwazi echazwa ngokuthi i-fuzzy, ne-Analytic Hierarchy Process (AHP) ne-Discrete-Event Simulation (DES). I-Artificial Intelligence (AI) eyingxenye ye-DSS isebenzise kakhulu uhlelo lwe-fuzzy CBR luhlangene ne-fuzzy AHP kulandelwa imithetho yolwazi olumayelana nohlobo lomsebenzi. I-CBR isetshenziswe ukumelela lezo zimo zamanani ezingaqondakali nezingaphelele kulezo zingxenye. I-AHP e-fuzzy yasetshenziswa ukuze kutholakale ulwazi kochwepheshe olubeka phambili lezo zingxenye. Ama-oda ezingxenye ezintsha kanye namasampuli asetshenziselwa ukuqeqesha avezwe njengamasha kanye nabekade evele ekhona ngokulandelana kusetshenziswa indlela eyaziwa ngokuthi yi-Object-Oriented (OO) method lapho kubuyiswa izinto noma kunezinqumo eziphakanyiswayo. Izindlela ezijwayelekile zokulandelanisa nokufanisa zisetshenziswe ohlelweni lokubuyisa izinto. Kusetshenziswe isu eliyi-DES ukuhlaziya ukusebenza kwezisombululo eziphakanyiswe yindlela ye-CBR e-fuzzy. Le ndlela iphinde yaveza izimo ezintathu eziphakanyiswe ukuba zibe yisisombululo esibalweni sezingxenye ezihlongozwayo. Ukusebenza kwalezi zimo kuhlungwe ngokusebenzisa indlela ye-DES kwase kuvela inqubo engcono. Ukungajwayeleki kwalolu cwaningo kusebenzise ingxube yezindlela ze-fuzzy CBR ne-DES ngoba lolu hlobo lwengxube belungakaze lusetshenziswe. Kusetshenziswe isibonelo sezibalo ekwethuleni ukusebenza kwale nqubo yokusebenza ehlongozwayo

Development of a decision support system for decision-based part/fixture assignment and fixture flow control.

Doctoral Degree. University of KwaZulu-Natal, Durban.An intense competition in a dynamic situation has increased the requirements that must be considered in the current manufacturing systems. Among those factors, fixtures are one of the major problematic components. The cost of fixture design and manufacture contributes to 10-20% of production costs. Manufacturing firms usually use traditional methods for part/fixture assignment works. These methods are highly resource consuming and cumbersome to enumerate the available fixtures and stabilise the number of fixtures required in a system. The aim of this study was to research and develop a Decision Support System (DSS), which was useful to perform a decision-based part/fixture assignment and fixture flow control during planned production periods. The DSS was designed to assist its users to reuse/adapt the retrieved fixtures or manufacture new fixtures depending upon the state of the retrieved fixtures and the similarities between the current and retrieved cases. This DSS combined Case-Based Reasoning (CBR), fuzzy set theory, the Analytic Hierarchy Process (AHP) and Discrete-Event Simulation (DES) techniques. The Artificial Intelligence (AI) component of the DSS immensely used a fuzzy CBR system combined with the fuzzy AHP and guiding rules from general domain knowledge. The fuzzy CBR was used to represent the uncertain and imprecise values of case attributes. The fuzzy AHP was applied to elicit domain knowledge from experts to prioritise case attributes. New part orders and training samples were represented as new and prior cases respectively using an Object-Oriented (OO) method for case retrieval and decision proposal. Popular fuzzy ranking and similarity measuring approaches were utilised in the case retrieval process. A DES model was implemented to analyse the performances of the proposed solutions by the fuzzy CBR subsystem. Three scenarios were generated by this subsystem as solution alternatives that were the proposed numbers of fixtures. The performances of these scenarios were evaluated using the DES model and the best alternative was identified. The novelty of this study employed the combination of fuzzy CBR and DES methods since such kinds of combinations have not been addressed yet. A numerical example was illustrated to present the soundness of the proposed methodological approach.Please refer to the PDF for author's keywords

Enhanced Industrial Machinery Condition Monitoring Methodology based on Novelty Detection and Multi-Modal Analysis

This paper presents a condition-based monitoring methodology based on novelty detection applied to industrial machinery. The proposed approach includes both, the classical classification of multiple a priori known scenarios, and the innovative detection capability of new operating modes not previously available. The development of condition-based monitoring methodologies considering the isolation capabilities of unexpected scenarios represents, nowadays, a trending topic able to answer the demanding requirements of the future industrial processes monitoring systems. First, the method is based on the temporal segmentation of the available physical magnitudes, and the estimation of a set of time-based statistical features. Then, a double feature reduction stage based on Principal Component Analysis and Linear Discriminant Analysis is applied in order to optimize the classification and novelty detection performances. The posterior combination of a Feed-forward Neural Network and One-Class Support Vector Machine allows the proper interpretation of known and unknown operating conditions. The effectiveness of this novel condition monitoring scheme has been verified by experimental results obtained from an automotive industry machine.Postprint (published version

Implementation of a robotic flexible assembly system

As part of the Intelligent Task Automation program, a team developed enabling technologies for programmable, sensory controlled manipulation in unstructured environments. These technologies include 2-D/3-D vision sensing and understanding, force sensing and high speed force control, 2.5-D vision alignment and control, and multiple processor architectures. The subsequent design of a flexible, programmable, sensor controlled robotic assembly system for small electromechanical devices is described using these technologies and ongoing implementation and integration efforts. Using vision, the system picks parts dumped randomly in a tray. Using vision and force control, it performs high speed part mating, in-process monitoring/verification of expected results and autonomous recovery from some errors. It is programmed off line with semiautomatic action planning

An integrated computer-aided modular fixture design system for machining semi-circular parts

Productivity is one of the most important factors in manufacturing processes because of the high level of market competition. In this regard, modular fixtures (MFs) play an important role in practically improving productivity in flexible manufacturing systems (FMSs) due to this technology using highly productive computer numerical control (CNC) machines. MFs consist of devices called jigs and fixtures for accurately holding the workpiece during different machining operations. The design process is complex, and traditional methods of MF design were not sufficiently productive. Computer-aided design (CAD) software has rapidly improved as a result of the development of computer technology, and has provided huge opportunities for modular fixture designers to use its 3D modelling capabilities to develop more automated systems. Computer-aided fixture design (CAFD) systems have become automated by the use of artificial intelligence (AI) technology. This study will investigate the further improvement of automated CAFD systems by using AI tools. In this research, an integrated CAFD is developed by considering four main requirements: · a 3D model of the workpiece, · an expert system, · assembly automation of MFs, · an efficient feature library. The 3D model is an important factor that can provide the appropriate specification of the workpiece; SolidWorks is used the CAD environment for undertaking the 3D modelling in this study. The expert system is applied as a tool to make right decisions about the CAFD planning process, including locating and clamping methods and their related element selection. This helps achieve a feasible fixture design layout. SolidWorks API and Visual Basic programming language are employed for the automating and simulation of the assembly process of MFs. A feature library of modular fixture elements is constructed as a means to simplify the fixture design process

COMPARATIVE STUDY ONMANUFACTURING PROCESSES OFA SWIRLER CASING

Process planning is a systematic method to determine the engineering processes and systems to manufacture a product competitively and economically. In this project, the process plan is subjected to analysis for fabrication process of swirler by machining and casting. The scope of study covered process sequence, machine tools and fixtures. The comparative study of fabrication swirler casing will be based on the analysis from machining and casting process plan that include the process time and cost consumption. Most of data for this study were obtained from literatures and experiments. The total process cost by casting is lower than machining process with RM 10113 for casting and RM 12578 for machining. However, total estimated process time for casting higher than machining which are 151 hr and 139.6 hr respectively. These findings have significant implications for the commercial application of manufacturing processes

Competitive high variance, low volume manufacturing with robot manipulators

Competitive robotized manufacturing of high specter variance, low volume product lines represents market opportunities for manufacturing companies, but cost-efficient production is challenging. In this paper, we present two main industry use cases which represent key challenges to be solved for cost-efficient low-volume, high-variance production. The use cases are found in collaboration with three manufacturing companies. We identify and describe these challenges which include perception and manipulation with shiny/high-reflectivity parts, human-machine interfaces for robot reconfiguration and calibration between simulated and real-world environments. In this paper, we present new methods for meeting these challenges: machine vision for handling sensor data with low quality in robot manipulation, automated robot programming based on CAD-models and automated calibration. Moreover, we implement and demonstrate the methods on the two identified industry use cases for robotized assembly.acceptedVersio