Maintainability Analysis Visualization System and Its Development in AutoCAD Environment

Maintainability is an important design characterist ic , and also is a design parameter. The essence of maintainability design is to ex pect and solve possible maintenance problems at early stage during product devel opment, maintainability must be designed into product simultaneously. But curren t maintainability methods do not support concurrent engineering. This paper anal yzes the deficiencies of existing maintainability technologies, and introduces t he concept of maintainability analysis visualization. The two major deficiencies are that maintainability analysis time lags behind product design, and the main tainability techniques do not accommodate modern product design concepts. After this, the infrastructure of maintainability analysis visualization system is dis cussed. The system are composed of four subsystems, they are product modeling, d isassembly process modeling, simulation and analysis, and user interface. Produc t modeling is accomplished with the capability of CAD system, disassembly proces s modeling is mainly a reason process based on assembly information. The system is integrated into AutoCAD so that designer can timely analyze their product des ign from the viewpoint of easy maintenance. Human model generation function, hum an task analysis function, tool analysis function, visibility analysis function is implemented, disassembly process planning function is still under developing.

Softw are Maintainability Evaluation Based on Software Configuration Management Data

As one of the most important attributes of software quality, software maintainability has been widely recognized.However,the existing maintainability evaluation methods are mostly based on subjectively judgment. Thus it is inapplicable or unbelievable. To evaluate software maintainability objectively,the software configuration management( SCM) data are collected to establish a maintainability model. Based on the hidden Markov chain( HMC), a three-state maintainability estimation model is constructed. To validate the feasibility of the model,a real software example of software maintenance activity is given and the result from the example shows the effectiveness of the proposed method.

Intellectualization of Design Technique for Product Maintainability

The basis of the two guideline lectures for maintainability design, namely''maintainability design criterions'' and ''cases compilation of maintainability design technique'' , anintellectualized architecture for combining rule-based reasoning (RBR) and case-based reasoning(CBR), is presented in this paper by analyzing from maintainability design procedure to the thoughtof our solving the maintainability design problem. Furthermore, the domain model and reasoningprocess is introduced and a representation of maintainability domain knowledge is given. Finally,the computer-aided engineering system for maintainability design (MCAES) which is based onintellectualized architecture is reported; its function and implementation is described.

Optimizing reliability, maintainability and testability parameters of equipment based on GSPN

Reliability, maintainability and testability （RMT） are important properties of equipment, since they have important influ- ence on operational availability and life cycle costs （LCC）. There- fore, weighting and optimizing the three properties are of great significance. A new approach for optimization of RMT parameters is proposed. First of all, the model for the equipment operation pro- cess is established based on the generalized stochastic Petri nets （GSPN） theory. Then, by solving the GSPN model, the quantitative relationship between operational availability and RMT parameters is obtained. Afterwards, taking history data of similar equipment and operation process into consideration, a cost model of design, manufacture and maintenance is developed. Based on operational availability, the cost model and parameters ranges, an optimization model of RMT parameters is built. Finally, the effectiveness and practicability of this approach are validated through an example.

Predicting the Maintainability of Open Source Software Using Design Metrics

This paper empirically investigates the relationships between 15 design metrics and maintainability of 148 Java open source software. The results show that size and complexity metrics are strongly related to the maintainability of open source software. However, cohesion and coupling, as currently captured by existing metrics, do not seem to have a significant impact on maintainability. When used together, these metrics can predict system maintainability fairly accurately （mean MREs below 30%）.

Electric Multiple Unit Bogie Maintainability Allocation with Interval Analysis and Fuzzy Evaluation Method

Based on the failure rate and design features allocation method,considering the multiple influential factors which affect electric multiple unit( EMU) bogies,maintainability allocation on EMU bogie was presented by interval analytic hierarchy analysis and fuzzy comprehensive assessment. The maintainability allocation model was established. Weight based on the influence degree of each factor on maintenance was assigned. Fuzzy interval numbers were used to substitute real numbers and express uncertain information.The maintenance weighting factors for each subsystem were calculated by fuzzy comprehensive assessment. Then the allocation method was applied to EMU bogie. The results show that the method is feasible. The problem difficult to quantify for EMU bogie maintenance allocation is solved effectively.

Clustering-based maintainability demonstration for complex systems with a mixed maintenance time distribution

During maintainability demonstration,the maintenance time for complex systems consisting of mixed technologies generally conforms to a mixture distribution.However existing maintainability standards and guidance do not explain explicitly how to deal with this situation.This paper develops a comprehensive maintainability demonstration method for complex systems with a mixed maintenance time distribution.First of all,a K-means algorithm and an expectation-maximization(EM)algorithm are used to partition the maintenance time data for all possible clusters.The Bayesian information criterion(BIC)is then used to choose the optimal model.After this,the clustering results for equipment are obtained according to their degree of membership.The degree of similarity for the maintainability of different kinds of equipment is then determined using the projection method.By using a Bootstrap method,the prior distribution is obtained from the maintenance time data for the most similar equipment.Then,a test method based on Bayesian theory is outlined for the maintainability demonstration.Finally,the viability of the proposed approach is illustrated by means of an example.

Softw are Maintainability Evaluation Based on Quantitive Model

Software maintainability is one of the most important factors of software quality,but it is seriously difficult to evaluate the maintainability. Without evaluation,it is impossible to control. To estimate software maintainability state,parameter system of software was built up and maintainability state was defined into three states.Thought of application on maintainability evaluation based on hidden Markov chain( HMC) and fuzzy inference was presented.Three-state maintainability estimation model was constructed. To testify the feasibility of the model, a real example of software maintenance activity was carried out and the result from the example validated that the results of this study were applicable.

Softw are Maintainability Prediction with UML Class Diagram

Software system can be classified into many function modules from the perspective of user. Unified modeling language( UML) class diagram of each function module was extracted,and design characteristic metrics which influenced software maintainability were selected based on UML class diagram.Choosing metrics of UML class diagram as predictors,and mean maintenance time of function module was regarded as software maintainability parameter. Software maintainability models were built by using back propagation( BP) neural network and radial basis function( RBF) neural network, respectively and were simulated by MATLAB. In order to evaluate the performance of models,the training results were analyzed and compared with leaveone-out cross-validation and model performance evaluation criterion. The result indicated that RBF arithmetic was superior to BP arithmetic in predicting software maintainability.

Product Maintainability Design Method and Support Tool Based on Feature Model

Maintainability is an important character which is given by product design process. The maintainability design criteria and measure index used in product maintainability analysis are summarized and discussed in this paper. A product maintainability design method is studied by integrating the product feature model, maintainability design criteria with measure index. Product feature model can be built on the basis of the product feature library quickly. Product feature library for steam turbine design is created by using SolidWorks design library origination structure. A methodology which supports the design and development of product maintainability design support tool (PMDSTs) is put forward. The function of PMDSTs is designed by using UML (Unified Modeling Language) use case diagram, it is developed by using VC++ 6.0. The maintainability analysis application case of steam turbine-generator system is given at last.

Knowledge Engineering Application in maintainability Design and Analysis

With the development of automatic design and artificial intelligence techniques, intelligence already have been applied in maintainability design and analysis of military /commercial equipment. Expert system has been used in diagnosis, design analysis, testability design, etc. In this paper, we will discuss about knowledge engineering application in two types. One is integrated knowledge base system for maintainability, it's the baseline of concurrent, intelligence design and it's also suitable for the present situation in China that there is lack of maintainability engineers. The other application is expert system for formulating maintainability design criteria and it can take full advantage of experts' experiences and past cases. Under development trend of intelligence design, these two systems will have good effects on maintainability design and analysis.

Maximum Likelihood Method of Maintainability Growth Evaluation for Weapon Equipment

The evaluation of maintainability growth plays an important role in improving materiel system effectiveness. Based on field maintenance information, a maximum likelihood model is put forward to evaluate the maintainability growth at the system level and sub-system level. A maximum likelihood function can be built after all the maintenance data can be divided into the following three categories： incomplete repair time data, complete repair time data, super-complete repair time data. According to the actual situation of the maintenance data, an appro- priate approximation can be made and the approximate analytical solution can be obtained. The values of u and can be obtained as the corresponding estimated value of unknown parameters. By effectively mining the latent sampie information, the maintainability growth evaluation is logical and reasonable.

An Improved Functional Hierarchy Frame Model for System Maintainability

By means of analogy, this paper analyses the present functional hierarchyframe model for system maintainability, and presents an improved model. Practical applicationindicates that the improved model is visualized, more convenient and perfected over the perviousmodels.

A Maintainability Prediction Method Considering Environmental Impacts and Cost

Maintainability prediction is one kind of primary maintainability action. Design deficiency would be found through predicting maintainability parameters under certain conditions. Now a maintainability prediction method that mainly considers maintenance time or maintenance man hour is a kind of prediction method with a single index. With increasing product complexity and people's environmental consciousness, more attention is paid to environment impacts and maintenance cost or resource consumption in the maintenance process. It is necessary for a maintainability prediction method that can predict maintenance cost and maintenance environmental impacts. A new maintainability prediction method is presented in this paper based on analyzing existing maintainability prediction methods. The method is MABTCE(maintenance activity based timing/costing/environment impact assessment) and can predict maintenance time, maintenance costing and maintenance environmental impacts and then improve maintainability design with prediction results.

Measuring code maintainability with deep neural networks

The maintainability of source code is a key quality characteristic for software quality.Many approaches have been proposed to quantitatively measure code maintainability.Such approaches rely heavily on code metrics,e.g.,the number of Lines of Code and McCabe’s Cyclomatic Complexity.The employed code metrics are essentially statistics regarding code elements,e.g.,the numbers of tokens,lines,references,and branch statements.However,natural language in source code,especially identifiers,is rarely exploited by such approaches.As a result,replacing meaningful identifiers with nonsense tokens would not significantly influence their outputs,although the replacement should have significantly reduced code maintainability.To this end,in this paper,we propose a novel approach(called DeepM)to measure code maintainability by exploiting the lexical semantics of text in source code.DeepM leverages deep learning techniques(e.g.,LSTM and attention mechanism)to exploit these lexical semantics in measuring code maintainability.Another key rationale of DeepM is that measuring code maintainability is complex and often far beyond the capabilities of statistics or simple heuristics.Consequently,DeepM leverages deep learning techniques to automatically select useful features from complex and lengthy inputs and to construct a complex mapping(rather than simple heuristics)from the input to the output(code maintainability index).DeepM is evaluated on a manually-assessed dataset.The evaluation results suggest that DeepM is accurate,and it generates the same rankings of code maintainability as those of experienced programmers on 87.5%of manually ranked pairs of Java classes.

An empirical study on the complexity, security and maintainability of Ethereum-based decentralized applications (DApps)

The Ethereum blockchain’s smart contract is a programmable transaction that performs general-purpose computations and can be executed automatically on the blockchain.Leveraging this component,blockchain technology(BT)has grown beyond the scope of cryptocurrencies and can now be applicable in various industries other than finance.In this paper,we investigated the current trends in Ethereum-based decentralized applications(DApps)to be able to categorize and analyze the DApps to measure the complexity of smart contracts behind them,their level of security and their correlation to the maintainability of the DApps.We leveraged the source code analysis,security analysis,and the developmental metadata of the DApps to infer this correlation.Based on our findings,we concluded that the maintainability of Ethereum DApps is proportional to the code size,number of functions,and,most importantly,the number of outgoing invocations and statements in the smart contracts.

COMPLEXITY OF SYSTEM MAINTAINABILITY ANALYSIS BASED ON THE INTERPRETIVE STRUCTURAL MODELING METHODOLOGY： TRANSDISCIPLINARY APPROACH

This paper outlines a diagnostic approach to quantify the maintainability of a Commercial off-the-Shelf （COTS）-based system by analyzing the complexity of the deployment of the system components. Interpretive Structural Modeling （ISM） is used to demonstrate how ISM supports in identifying and understanding interdependencies among COTS components and how they affect the complexity of the maintenance of the COTS Based System （CBS）. Through ISM analysis we have determined which components in the CBS contribute most significantly to the complexity of the system. With the ISM, architects, system integrators, and system maintainers can isolate the COTS products that cause the most complexity, and therefore cause the most effort to maintain, and take precautions to only change those products when necessary or during major maintenance efforts. The analysis also clearly shows the components that can be easily replaced or upgraded with very little impact on the rest of the system.

New pharmacological tools:the use of diterpenes to promote adult hippocampal neurogenesis

Tissue regeneration maintains homeostasis and preserves the functional features of each tissue.However,not all tissues show a strong repairing capacity.This is the case of the central nervous system.It is now well established that the generation of new functional neurons from stem cells in the adult brain occurs in specific regions of the brain of different species such as rodents,birds,primates,and humans(Eriksson et al.,1998).

MWCNT Doped Reverse-Mode Polymer Network Liquid Crystals with Frequency Response Property

Polymer-liquid crystals(PLCs)are common materials for smart windows.However,PLC smart windows usually require high driving voltage to maintain transparency.We synthesized a novel PLC smart film by doping multi-wall carbon nanotubes(MWCNTs)into a reverse-mode polymer network liquid crystal(R-PNLC).

Aging impairs the osteocytic regulation of collagen integrity and bone quality

Poor bone quality is a major factor in skeletal fragility in elderly individuals.The molecular mechanisms that establish and maintain bone quality,independent of bone mass,are unknown but are thought to be primarily determined by osteocytes.We hypothesize that the age-related decline in bone quality results from the suppression of osteocyte perilacunar/canalicular remodeling(PLR),which maintains bone material properties.We examined bones from young and aged mice with osteocyte-intrinsic repression of TGFβsignaling(TβRII^(ocy−/−))that suppresses PLR.The control aged bone displayed decreased TGFβsignaling and PLR,but aging did not worsen the existing PLR suppression in male TβRII^(ocy−/−)bone.This relationship impacted the behavior of collagen material at the nanoscale and tissue scale in macromechanical tests.The effects of age on bone mass,density,and mineral material behavior were independent of osteocytic TGFβ.We determined that the decline in bone quality with age arises from the loss of osteocyte function and the loss of TGFβ-dependent maintenance of collagen integrity.