Maintainability Index Research Articles

Dependability Assessment of a Dual-Axis Solar Tracking Prototype Using a Maintenance-Oriented Metric System

This study presents a numerical method for evaluating the maintainability of a dual-axis solar tracking system that can be deployed in residential areas for improved energy production. The purpose of this research manuscript is threefold. It targets the following objectives: (i) First, we present the construction of a self-sufficient dual-axis solar tracking system based on a low-power electronic schematic that requires only one motor driver to control the azimuth and elevation angles of the photovoltaic (PV) panel. The automated system’s main electronic equipment comprises 1 × Arduino Mega2560 microcontroller unit (MCU), 1 × TB6560 stepper driver module, 2 × stepper motors, 2 × relay modules, 1 × solar charge controller, 1 × accumulator, and 1 × voltage convertor. Additional hardware components such as photoresistors, mechanical limit switches, rotary encoders, voltage, and current sensors are also included to complete the automation cycle of the solar tracking system. (ii) Second, the Arduino Mega 2560 prototyping board is replaced by a custom-made and low-cost application-specific printed circuit board (ASPCB) based on the AVR controller. The MCU’s possible fault domain is then further defined by examining the risks of the poor manufacturing process, which can lead to stuck-at-0 (Sa0) and stuck-at-1 (Sa1) defects. Besides these issues, other challenges such as component modularity, installation accessibility, and hardware failures can affect the automated system’s serviceability. (iii) Third, we propose a novel set of maintenance-oriented metrics that combine the previously identified variables to provide a maintainability index (MI), which serves as a valuable tool for evaluating, optimizing, and maintaining complex systems such as solar tracking devices. The experimental data show that the computed MI improves the system’s maintainability and enhances repair operations, increasing uptime.

Source-Code Maintainability Evaluation Model for Software Products

The maintainability index (MI) has been proposed to calculate a single number which expresses the maintainability of a system. This article presents a model for evaluating the maintainability of software products. The model improves the shortcomings observed in the maintainability assessment approaches in the quality assessment models SQuaRE (ISO25000), ISO 9126, Squale and the FCM standard. Its main innovation is to take into account the importance of entities in the system when calculating the maintainability score. This implies that the same type of defect will have a different score depending on the entity presenting it. Seven experts with several years of experience evaluated the model. They confirmed the effectiveness and usability of the model. Then, we compared our model with the Squale maintainability index and the classical maintainability index. The results show no correlation between these models. The implications are that each method gives a slightly different view of maintainability.

УДОСКОНАЛЕНИЙ МЕТОД ПІДВИЩЕННЯ ЗРУЧНОСТІ СУПРОВОДУ ЗА УМОВ ЗАСТОСУВАННЯ БЕЗСЕРВЕРНОЇ АРХІТЕКТУРИ

It has been found that achieving high quality indicators, in particular maintainability [1], is becoming an increasing problem due to the emergence of new technologies, in particular cloud technologies and serverless architecture. Despite this, requirements for the level of ease of support are only growing, and this characteristic of software quality is receiving more and more attention. The numerous tools, methodologies, approaches and methods of software development previously developed by the scientific and engineering communities for its enhancement are insufficiently effective under the existing conditions. It was confirmed that the use of an improved method to increase the convenience of maintenance under the conditions of using a serverless architecture is an urgent necessity. The current state of approaches to increasing the convenience of support was analyzed. As a result, Clean Architecture was chosen as the best existing approach because it inherits the main advantages of competing approaches, provides clearer instructions, and covers a wider range of the development process. Although it provides significant improvements in key maintainability characteristics such as modularity and reusability, there are still other characteristics that need to be considered, such as analyzability. modifiability and testability. The requirements for the improved method were determined and an improved method was developed to increase the convenience of support in the conditions of using a serverless architecture, which satisfies all the previously formulated requirements. In particular, this approach improves work with dependencies, provides a basic structure for components, increases cohesion and reduces coupling of functionality. To evaluate the effectiveness of the created method, two implementations of the same project were developed: based on Clean Architecture and using the improved method. The following maintainability assessment metrics were calculated for them: the Maintainability Index (MI) and its variation, in which only files with an index of less than 100 are taken into account, which we will call the Filtered Maintainability Index FMI. Analyzing the obtained results, it was found that the application of the improved method increases the value of the first metric by 2.1 % and the second by 8.3 %. In this way, the effectiveness of the developed improved method was proven.

Firefly-Based Maintainability Prediction for Enhancing Quality of Software

In a broad spectrum, software metrics play a vital role in attribute assessment, which successively moves software projects. The metrics measure gives many crucial facets of the system, enhancing the system quality of software developed. Moreover, maintenance is the correction process that works out in the software system once the software is initially made. The noteworthy characteristic of any software is ‘change,’ and as a result, additional concern ought to be taken in developing software. So, the software is expected to be modified effortlessly (maintainable). Predicting software maintainability is still challenging, and accurate prediction models with low error rates are required. Since there are so many modern programming languages on the horizon. To accurately measure software maintainability, new techniques have to been introduced. This paper proposes a maintainability index (MI) by considering various software metrics by which the error gets minimized. It also intends to adopt a renowned optimization algorithm, namely Firefly (FF), for the optimum result. The proposed Base Model-FF is compared to other traditional models like BM-Differential Evolution (BM-DE), BM-Artificial Bee Colony (BM-ABC), BM-Particle Swarm Optimization (BM-PSO), and BM-Genetic Algorithm (BM- GA) in terms of performance metrics like Differential ratio, correlation coefficient, and Random Mean Square Error (RMSE).

A Nonlinear Weight-Optimized Maintainability Index of Software Metrics by Grey Wolf Optimization

Maintainability index (MI) is a software metric that offers measurements of the maintainability before release of the software by facilitating several substantial features of the system. In general, there is a common formula for determining the MI for all the software metrics to ensure the system's reliability. As it does not provide appropriate results regarding the reliability of the system, it is essential to focus on the next level of MI of software. Hence, this paper intends to allot an optimal weight and a constant to each software metric, which is optimized by grey wolf optimization (GWO). As a result, it can provide a new variant of MI by proposed enhanced model-GWO (EM-GWO). This optimized MI can ensure the efficiency of the respective software in such a way that it can provide an enhanced score from the system. Further, the proposed method is compared with conventional models such as enhanced model-generic algorithm (EM-GA), EM-particle swarm optimization (PSO), EM-ant bee colony (ABC), EM-differential evolution (DE), and EM-fire fly (FF), and the results are obtained.

Comparing Maintainability Index, SIG Method, and SQALE for Technical Debt Identification

There are many definitions of software Technical Debt (TD) that were proposed over time. While many techniques to measure TD emerged in recent times, there is still not a clear understanding about how different techniques compare when applied to software projects. The goal of this paper is to shed some light on this aspect, by comparing three techniques about TD identification that were proposed over time: (i) the Maintainability Index (MI), (ii) SIG TD models, and (iii) SQALE analysis. Considering 20 open source Python libraries, we compare the TD measurements time series in terms of trends and evolution according to different sets of releases (major, minor, and micro), to see if the perception of practitioners about TD evolution could be impacted. While all methods report generally growing trends of TD over time, there are different patterns. SQALE reports more periods of steady states compared to MI and SIG TD. MI is the method that reports more repayments of TD compared to the other methods. SIG TD and MI are the models that show more similarity in the way TD evolves, while SQALE and MI are less similar. The implications are that each method gives slightly a different perception about TD evolution.

Design of testing framework for code smell detection (OOPS) using BFO algorithm

Detection of bad smells refers to any indication in the program code of a execution that perhaps designate a issue, maintain the software and software evolution. Code Smell detection is a main challenging for software developers and their informal classification direct to the designing of various smell detection methods and software tools. It appraises 4 code smell detection tool in software like as a in Fusion, JDeodorant, PMD and Jspirit. In this research proposes a method for detection the bad code smells in software is called as code smell. Bad smell detection in software, OOSMs are used to identify the Source Code whereby Plug-in were implemented for code detection in which position of program initial code the bad smell appeared so that software refactoring can then acquire position. Classified the code smell, as a type of codes: long method, PIH, LPL, LC, SS and GOD class etc. Detection of the code smell and as a result applying the correct detection phases when require is significant to enhance the Quality of the code or program. The various tool has been proposed for detection of the code smell each one featured by particular properties. The main objective of this research work described our proposed method on using various tools for code smell detection. We find the major differences between them and dissimilar consequences we attained. The major drawback of current research work is that it focuses on one particular language which makes them restricted to one kind of programs only. These tools fail to detect the smelly code if any kind of change in environment is encountered. The base paper compares the most popular code smell detection tools on basis of various factors like accuracy, False Positive Rate etc. which gives a clear picture of functionality these tools possess. In this paper, a unique technique is designed to identify CSs. For this purpose, various object-oriented programming (OOPs)-based-metrics with their maintainability index are used. Further, code refactoring and optimization technique are applied to obtain low maintainability Index. Finally, the proposed scheme is evaluated to achieve satisfactory results. The results of the BFOA test defined that the lazy class caused framework defects in DLS, DR, and SE. However, the LPL caused no framework defects what so ever. The consequences of the connection rules test searched that the LCCS (Lazy Class Code Smell) caused structured defects in DE and DLS, which corresponded to the consequences of the BFOA test. In this research work, a proposed method is designed to verify the code smell. For this purpose, different OOPs based Software Metrics with their MI (Maintainability Index) are utilized. Further Code refactoring and optimization method id applied to attained the less maintainability index and evaluated to achieved satisfactory results.

Performance of Maintainability Index prediction models: a feature selection based study

Numerous design metrics have been studied in the literature to assess software performance and future maintainability issues. Maintainability Index (MI) is an indicator of software maintenance efforts, but that can be computed only after the complete code is developed. MI is complex to calculate and hence has been estimated by several researchers using design metrics. Various prediction models are built using different sets of design metrics as independent variables. Further, researchers have used different prediction models for predicting software maintainability. To identify most prominent design metrics and the prediction models that show consistent performance irrespective of the choice of design metrics, the present empirical study is performed on datasets of 26 open source Java projects with more than one thousand and six hundred class hierarchies. Seven feature selection methods are applied to find out the most prominent metrics that are selected by majority of methods. Then four most frequently used prediction models namely, multi linear regression, multilayer perceptron, support vector regression and M5P regression tree are analyzed for their performance with respect to initial selection of design metrics. The study concludes that MIF, MaxDIT, LCC, TCC are the most significant predictors for Maintainability Index of class hierarchies. Among the all the prediction models, support vector regression model exhibits the best performance both with respect to the choice of metrics and irrespective of initial metric selection.

Maintenance as a Guarantee for Roofing Performance in Buildings with Heritage Value

This paper is the result of the research conducted at the University of Porto and follows up on the Ph.D. thesis developed by the second author, which deals with the issue of the non-inclusion of maintenance at the time of architectural design, and of support instruments for the project author to help him in this endeavor. This work is intended to apply a guiding methodology to the design process that includes building maintenance benchmarked by the service behavior with regard to pitched roofs. The development of a decision support model enabled to obtain a maintainability index (MI) to be identified and results from the combination of two indicators: the importance indicator (Iid) and the easiness indicator (Eid). The results achieved enable the project author to have a support tool for making decisions that affect the building behavior in service as well the act of the preservation of heritage value.

Correlations and Weights of Maintainability Index (MI) of Open source Linux Kernel Modules

Software development and usage become very important in many aspects of our lives, so that the application of software metrics becomes more important. Software metrics are used to give valuable information for the development of software. This paper focus on the study of the maintainability of an open source operating system “Linux”. Computing the relative weight of each maintainability parameter. The research was conducted on Linux Kernel modules (V.3.9.2). The research performed on 837 functions from selected modules to compute a maintainability index “MI” for each function and module. Also calculate the correlation between each parameter for Maintainability Index (MI) with the other parameters and with the MI itself. The parameters of MI are Line of Code (LOC), Cyclomatic Complexity (CC), and Halstead Volume (HV). Practically approved that, first the Line of Code and Cyclomatic Complexity values are distributed normally; but Halstead Volume is distributed uniformly. Secondly, there is an interconnection between Line of Code, Cyclomatic Complexity, Halstead Volume and Maintainability Index. Finally the most important parameter which affects maintainability index is Line of Code, then Cyclomatic Complexity and lastly the Halstead Volume. General Terms Software Quality

Measuring Maintainability Index of a Software Depending on Line of Code Only

Maintainability Index (MI) has been proposed to calculate a single number that expresses the maintainability of a system. MI is calculated as a factored formula consisting of Lines of Code (LOC), Cyclomatic Complexity (CC)and Halstead Volume (HV). In this paper, we present a new method to find MI with respect to LOC only. To validate the method Measuring Maintainability Index Software (MMIS) is developed, that first finds MI with respect to (LOC, CC, and HV); secondly find MI with respect to LOC only. As the result has been compared they were closed but the new method was easy to understand, fast to count, and independent on the program language. In addition to many well-known programs, the MMIS was used to test itself and the result demonstrates that the program used to calculate MI value with respect to LOC only tend to be maintainable while the lexical analyzer the major step in calculating MI with regard to (LOC, HV, CC) is difficult to maintain.