Design for Software Risk Management Using Soft Computing and Simulated Biological Approach

Prediction of faults in a proposed software is helpful in deciding the amount of effort to be given for software development. We observed that, a good number of authors hypothesized that the performance of fault prediction model depends on the source code metrics which are used as input of the model. Feature selection technique is a process of selecting suitable set of source code metrics which may improve the performance of fault prediction model. In this work, genetic algorithm (GA) has been applied as feature selection technique to select the suitable set of source code metrics. This selected set of source code metrics are used as requisite input data to develop a classifier using five different classification techniques such as logistic regression, extreme learning machine, support vector machine (SVM) with three different kernel functions (linear, polynomial, and radial basis kernel functions) in order to predict the faulty and non-faulty classes. In this study, we propose a cost evaluation framework to perform cost based analysis for evaluating the effectiveness of fault prediction model. We perform experiments on thirty number of Java Open Source projects. From the obtained results, it is observed that the model developed using selected set of source code metrics obtained better result as compared to all metrics. From costs analysis framework, it is observed that the developed fault prediction model is best suitable for software with % of faulty classes less than the threshold value depending on fault identification efficiency (low-46.44%, median-45.37%, and high-36.63%).

Context: A large number of source code metrics are reported in the literature. It is necessary to systematically collect, describe and classify source code metrics to support research and practice. Objective: We aim to utilize existing secondary studies to develop a catalog of source code metrics together with their descriptions. The catalog will also provide information about which units of code (e.g., operators, operands, lines of code, variables, parameters, code blocks, or functions) are used to measure the internal quality attributes and the scope on which they are collected. Method: We conducted a tertiary study to identify secondary studies reporting source code metrics. We have classified the source code metrics according to the measured internal quality attributes, the units of code used in the measures, and the scope at which the source code metrics are collected. Results: From 711 secondary studies, we identified 52 relevant secondary studies. We reported 423 source code metrics together with their descriptions and the internal quality attributes they measure. Source code metrics predominantly incorporate function as a unit of code to measure internal quality attributes. In contrast, several source code metrics use more than one unit of code when measuring internal quality attributes. Nearly 51% of the source code metrics are collected at the class scope, while almost 12% and 15% of source code metrics are collected at module and application levels, respectively. Conclusions: Researchers and practitioners can use the extensive catalog to assess which source code metrics meet their individual needs based on the description and classification scheme presented.

Mobile applications have been around for a long time and proved to be a new excited market where everyone want to engage themselves. They have become more important than Web pages nowadays. Companies are giving more preference to mobile apps as compared to Web sites because of their user friendliness , better visibility and ease of social networking. This paper compares static code metrics and process metrics for predicting defects in an open source mobile applications. Correlation coefficient, mean absolute error and root mean squared error with process metrics as predictors are significantly better than with code metrics as predictors. Also the combined model based on process and code metrics is better than the model based on code metrics. It is shown that process metrics based defect prediction models are better for mobile applications in all 7 machine learning techniques used for modelling.

Fault localization aims to support the debugging activities of human developers by highlighting the program elements that are suspected to be responsible for the observed failure. Spectrum Based Fault Localization (SBFL), an existing localization technique that only relies on the coverage and pass/fail results of executed test cases, has been widely studied but also criticized for the lack of precision and limited effort reduction. To overcome restrictions of techniques based purely on coverage, we extend SBFL with code and change metrics that have been studied in the context of defect prediction, such as size, age and code churn. Using suspiciousness values from existing SBFL formulas and these source code metrics as features, we apply two learn-to-rank techniques, Genetic Programming (GP) and linear rank Support Vector Machines (SVMs). We evaluate our approach with a ten-fold cross validation of method level fault localization, using 210 real world faults from the Defects4J repository. GP with additional source code metrics ranks the faulty method at the top for 106 faults, and within the top five for 173 faults. This is a significant improvement over the state-of-the-art SBFL formulas, the best of which can rank 49 and 127 faults at the top and within the top five, respectively.

A significant number of Free Software projects has been widely used and considered successful. However, there is an even larger number of them that cannot overcome the initial step towards building an active community of users and developers. In this study, we investigated whether there are relationships between source code metrics and attractiveness, i.e., the ability of a project to attract users and developers. To verify these relationships, we analyzed 6,773 Free Software projects from the SourceForge.net repository. The results indicated that attractiveness is indeed correlated to some source code metrics. This suggests that measurable attributes of the project source code somehow affect the decision to contribute and adopt a Free Software project. The findings described in this study show that it is relevant for project leaders to monitor source code quality, most specifically a few objective metrics, since these can have a positive influence in their chances of forming a community of contributors and users around the software, enabling further enhancement in its quality.

Image segmentation is the method of partitioning an image into a group of pixels that are homogenous in some manner. The homogeneity dependents on some attributes like intensity, color etc. Segmentation being a pre-processing step in image processing have been used in the number of applications like identification of objects to medical images, satellite images and much more. The taxonomy of an image segmentation methods collectively can be divided among two categories Traditional methods and Soft Computing (SC) methods. Unlike Traditional methods, SC methods have the ability to simulate human thinking and are flexible to work with their ownership function, have been predominantly applied to the task of image segmentation. SC techniques are tolerant of partial truth, imprecision, uncertainty, and approximations. Soft Computing approaches also having advantages of providing cost-effective, high performance and steadfast solutions. In this survey paper, our emphasis is on core SC approaches like Fuzzy logic, Artificial Neural Network, and Genetic Algorithm used for image segmentation. The contribution lies in the fact to present this paper to the researchers that explore state-of-the-art elaboration of almost all dimensions associated with the image segmentation. The idea is to encapsulate various aspects like emerging topics, methods, evaluation parameters, the problem associated with different type of images, databases, segmentation applications, and other resources so that, it could be advantageous for researchers to make effort in developing new methods for segmentation. The paper accomplishes with findings and concluding remarks.

Bug prediction is an approach that helps make bug detection more automated during software development. Based on a bug dataset a prediction model is built to locate future bugs. Bug datasets contain information about previous defects in the code, process metrics, or source code metrics, etc. As code smells can indicate potential flaws in the source code, they can be used for bug prediction as well.

Software project development is very crucial, and measuring the exact cost and effort of development is becoming tedious and challenging. Organizations are trying to wind up their project of software development within the agreed budget and schedule successfully. Traditional practices are inadequate to achieve the current needs of the software industry. Underestimation and overestimation of software development effort lead to financial implications in the form of resources, cost of staffing, and budget of developing the software project. Soft computing (SC) approaches and tools deliver an addition of techniques for anticipating resistance to the deception, defect, incomplete truth for traceability and ambiguity, low arrangement cost, and strength. A large amount of SC approaches is prevailing in the literature to accomplish way-out to difficulties precisely, practically, and speedily. The approaches of SC can give better prediction, high performance, and dynamic behavior. SC deals with computational intelligence which integrates the concept of agent paradigm and SC. The proposed study presents a systematic literature review (SLR) of the approaches, tools, and techniques of SC used in the literature. The study presented a comprehensive review by searching the defined keywords in the popular libraries, filtered the paper, and obtained most relevant papers. After the selection of the papers, the quality assessment process of the included papers has been done in order to determine the relevancy of the papers. The study will help researchers in the area of research to devise novel ideas and solutions to overcome the existing issue on the basis of this study as evidence of the literature.

Introduction: Soft Computing approaches are becoming increasingly significant in today's environment for treating various heart diseases and presenting potential solutions. A cardiac pacemaker (CP), known as an artificial pacemaker, can treat a heart condition. Medical devices that can be implanted to regulate abnormal heart rhythms are known as artificial pacemakers. Objectives: CP needs a high level of precision and performance. It is problematic to keep CP under control. This paper goals to compare and contrast several current and cutting-edge actors-critics, policy grade soft computing (SC) techniques for unremitting control of the artificial CPs, both on and off-policy. Methods: The proportional-integral-derivative (PID) controller can adapt to the problem domain using SC approaches named SC-PID. Finding the correct balance between the exploration and exploitation stages is also critical. SC-PID becomes more influential, trustworthy, and efficient for pacemaker control. Results: Compared to the traditional PID controller techniques, SC-PID delivers a new appropriate controller approach for calculating heart rate (HR) to create a tighter match between real HR and a target profile. The above abilities make SC approaches more suitable for health care. With the introduction of low-cost, high concert digital CPUs and lower-cost memory chips, SC techniques, and their application areas will undoubtedly continue to expand. This study showed that the SC-PID controller is effective for heartbeat recovery. Conclusions: Results in this paper show that the SC-PID controller will be more helpful not only for the treatment of heart disease but also for improving the performance of implantable pacemakers in contrast to conventional PID controllers. This study underscores the potential of soft computing as a supportive tool in health care, aligning with Sustainable Development Goal (SDG) 3’s vision of good health and well-being for all.

The correct prediction of faulty modules or classes has a number of advantages such as improving the quality of software and assigning capable development resources to fix such faults. There have been different kinds of fault/defect prediction models proposed in literature, but a great majority of them makes use of static code metrics as independent variables for making predictions. Recently, process metrics have gained a considerable attention as alternative metrics to use for making trust-worthy predictions. The objective of this paper is to investigate different combinations of static code and process metrics for evaluating fault prediction performance. We have used publicly available data sets, along with a frequently used classifier, Naive Bayes, to run our experiments. We have, both statistically and visually, analyzed our experimental results. The statistical analysis showed evidence against any significant difference in fault prediction performances for a variety of different combinations of metrics. This reinforced earlier research results that process metrics are as good as predictors of fault proneness as static code metrics. Furthermore, the visual inspection of box plots revealed that the best set of metrics for fault prediction is a mix of both static code and process metrics. We also presented evidence in support of some process metrics being more discriminating than others and thus making them as good predictors to use.

This paper presents an approach for the application of clustering algorithms to uncover computer programming learning profiles by using evidence extracted from source code metrics. A system for automatic assessment of programming activities featuring capture of source code metrics was developed, in order to build a dataset containing metrics extracted from programs developed by beginners in a computing course. The dataset was submitted to three clustering algorithms. The results were promising when clustering students according to these indicators using the K-means algorithm.

Due to the constant evolution of technology, each day brings new programming languages, development paradigms, and ways of evaluating processes. This is no different with source code metrics, where there is always new metric classes. To use a software metric to support decisions, it is necessary to understand how to perform the metric collection, calculation, interpretation, and analysis. The tasks of collecting and calculating source code metrics are most often automated, but how should we monitor them during the software development cycle? Our research aims to assist the software engineer to monitor metrics of vulnerability threats present in the source code through a reference prediction model, considering that real world software have non-functional security requirements, which implies the need to know how to monitor these requirements during the software development cycle. As a first result, this paper presents an empirical study on the evolution of the Linux project. Based on static analysis data, we propose low complexity models to study flaws in the Linux source code. About 391 versions of the project were analyzed by mining the official Linux repository using an approach that can be reproduced to perform similar studies. Our results show that it is possible to predict the number of warnings triggered by a static analyzer for a given software project revision as long as the software is continuously monitored.

Maintenance is one of the most effort consuming activities in the software development lifecycle. Efficient maintenance within short release cycles depends highly on the underlying source code structure, in the sense that complex modules are more difficult to maintain. In this paper we attempt to unveil and discuss relationships between maintenance productivity, the structural quality of the source code and process metrics like the type of a release and the number of downloads. To achieve this goal, we developed a Bayesian Belief Network (BBN) involving several maintainability predictors and three managerial indices for maintenance (i.e., duration, production, and productivity) on 20 open source software projects. The results suggest that maintenance duration depends on inheritance, coupling, and process metrics. On the other hand maintenance production and productivity depend mostly on code quality metrics.

Web services are being embraced by IT industry in the recent past to enable rapid development of distributed systems with optimal cost. Web services in SOA are self-adaptable to context, which makes SOA widely recognized in IT system as the technology, which has the potential of improving the receptiveness of both business and IT organizations. Web services help in building Service Based Systems (SBS) like Paytm, Amazon, Paypal, e-bay etc. which evolves frequently to fit the new user requirements which impacts the evolvability and quality of software design. Similar to software systems built using other paradigms, Service based systems also suffer from bad or poor design choices as in anti-pattern, code smells etc. Anti-patterns are explicit structures in the design that indicates violation of fundamental design principles and negatively impact the design quality. Anti-patterns have obstructive influence on the maintainability and perception of software systems. Thus there is a rising need for the early prediction of anti-patterns and refactoring them to improve the software quality in terms of execution cost, maintenance cost and memory consumption. In this work, a frame work is proposed for significant feature selection from source code metrics which includes Wilcoxon signed rank test, Univariate logistic regression analysis and Cross-correlation analysis. Then the different sets of features from various steps along with the original source code metrics are considered and are used for anti-pattern detection using 13 machine learning algorithms. Experimental results show the approximation capability of different classifiers and data balancing techniques with the features selected from the various steps of feature validation framework in addition to the original features for developing anti-pattern prediction model. The results also shows that the prediction model built with by the ensemble techniques using the features obtained from proposed feature selection framework outperforms other techniques.KeywordsSoftware engineeringAnti-patternWeb serviceImbalanced dataService oriented architectureMachine learningPredictionSource code metric

Software defect prediction is an important part to ensure software quality and increase cost-efficiency. The core aspect prior to software defect prediction is software metrics. More research of process metrics existed and rapidly developed as the fact that process metrics can outperform code metrics. The development of process metrics is diverse aligning with various process metrics generated in the different publication using different dataset and granularity level, thus the development state of process metrics need to be further tracked. Recent research on software defect prediction using process metrics has been carried which complements or argues another studied related to the implementation of process metrics, dataset, and granularity level. This literature review has collected 17 literature studies that aim to depict the current research used of software defect prediction applying software process metrics, dataset, and granularity. Seven commonly used process metrics are identified such as NDC, NML, ADEV, EXP, LA, LD, and COMM. Process metrics development has been implemented in the previous research such as the application of aggregated process metrics and commits detailed information metrics. The selected research shows that 87% of research are using a public dataset while the rest are using a combined dataset. This result depicts the limited accessibility of private and industrial dataset for defect prediction. Following analysis of software granularity presents 4 levels of granularity used in selected research such as a functional, class, file, and features level. File-level defect predictions are mostly used for defect prediction in selected research and prove good defect prediction performance. The results of this literature review also identify the proposed studies which is software defect prediction using developed process metrics in the different datasets and features-level granularity to strengthen the previous research.