Improve Software Quality Research Articles

Reducing redundancy of test cases generation using code smell detection and refactoring

In software development life cycle (SDLC), the testing phase is important to test the functionalities of any software. In this phase, test cases are generated to test software functionalities. This paper presents an approach on how to detect and refactor code smells from the source codes of an Android application in order to reduce the redundancy in test case generation. Refactoring is one of the vital activities in software development and maintenance. Refactoring is a process of code alteration that aims to make structural modifications to the source code without altering any external functionality. These changes often improve software quality such as readability, execution time and maintainability. The proposed approach is then implemented and evaluated in order to determine its effectiveness in reducing the redundancy of test case generation.

RevRec: A two-layer reviewer recommendation algorithm in pull-based development model

Code review is an important process to reduce code defects and improve software quality. In social coding communities like GitHub, as everyone can submit Pull-Requests, code review plays a more important role than ever before, and the process is quite time-consuming. Therefore, finding and recommending proper reviewers for the emerging Pull-Requests becomes a vital task. However, most of the current studies mainly focus on recommending reviewers by checking whether they will participate or not without differentiating the participation types. In this paper, we develop a two-layer reviewer recommendation model to recommend reviewers for Pull-Requests (PRs) in GitHub projects from the technical and managerial perspectives. For the first layer, we recommend suitable developers to review the target PRs based on a hybrid recommendation method. For the second layer, after getting the recommendation results from the first layer, we specify whether the target developer will technically or managerially participate in the reviewing process. We conducted experiments on two popular projects in GitHub, and tested the approach using PRs created between February 2016 and February 2017. The results show that the first layer of our recommendation model performs better than the previous work, and the second layer can effectively differentiate the types of participation.

Shorter identifier names take longer to comprehend

Developers spend the majority of their time reading code, a process in which identifier names play a key role. Although many identifier naming styles exist, they often lack an empirical basis and it is not clear whether short or long identifier names facilitate comprehension. In this paper, we investigate the effect of different identifier naming styles (single letters, abbreviations, and words) on program comprehension. We conducted an experimental study with 72 professional C# developers who had to locate defects in source code snippets. We used a within-subjects design, such that each developer worked with all three versions of identifier naming styles, and we measured the time it took them to find a defect. We found that word identifiers led to a 19% increase in speed to find defects compared to meaningless single letters and abbreviations, but we did not find a difference between letters and abbreviations. The results of our study suggest that code is more difficult to comprehend when it contains only letters and abbreviations as identifier names. Words as identifier names facilitate program comprehension and may help to save costs and improve software quality.

Agile Acceptance Test-Driven Development of Clinical Decision Support Advisories: Feasibility of Using Open Source Software.

BackgroundMoving to electronic health records (EHRs) confers substantial benefits but risks unintended consequences. Modern EHRs consist of complex software code with extensive local configurability options, which can introduce defects. Defects in clinical decision support (CDS) tools are surprisingly common. Feasible approaches to prevent and detect defects in EHR configuration, including CDS tools, are needed. In complex software systems, use of test–driven development and automated regression testing promotes reliability. Test–driven development encourages modular, testable design and expanding regression test coverage. Automated regression test suites improve software quality, providing a “safety net” for future software modifications. Each automated acceptance test serves multiple purposes, as requirements (prior to build), acceptance testing (on completion of build), regression testing (once live), and “living” design documentation. Rapid-cycle development or “agile” methods are being successfully applied to CDS development. The agile practice of automated test–driven development is not widely adopted, perhaps because most EHR software code is vendor-developed. However, key CDS advisory configuration design decisions and rules stored in the EHR may prove amenable to automated testing as “executable requirements.”ObjectiveWe aimed to establish feasibility of acceptance test–driven development of clinical decision support advisories in a commonly used EHR, using an open source automated acceptance testing framework (FitNesse).MethodsAcceptance tests were initially constructed as spreadsheet tables to facilitate clinical review. Each table specified one aspect of the CDS advisory’s expected behavior. Table contents were then imported into a test suite in FitNesse, which queried the EHR database to automate testing. Tests and corresponding CDS configuration were migrated together from the development environment to production, with tests becoming part of the production regression test suite.ResultsWe used test–driven development to construct a new CDS tool advising Emergency Department nurses to perform a swallowing assessment prior to administering oral medication to a patient with suspected stroke. Test tables specified desired behavior for (1) applicable clinical settings, (2) triggering action, (3) rule logic, (4) user interface, and (5) system actions in response to user input. Automated test suite results for the “executable requirements” are shown prior to building the CDS alert, during build, and after successful build.ConclusionsAutomated acceptance test–driven development and continuous regression testing of CDS configuration in a commercial EHR proves feasible with open source software. Automated test–driven development offers one potential contribution to achieving high-reliability EHR configuration. Vetting acceptance tests with clinicians elicits their input on crucial configuration details early during initial CDS design and iteratively during rapid-cycle optimization.

Towards cognitive support for unit testing: A qualitative study with practitioners

Unit testing is an important component of software quality improvement. Several researchers proposed automated tools to improve this activity over the years. However, these research efforts have not been sufficient to help the practitioners to address some associated mental tasks. Motivated by this gap, we conducted a qualitative study of professionals with unit testing experience. The goal was to understand how to improve the cognitive support provided by the testing tools, by considering the practitioners’ perspective on their unit testing review practices. We obtained the responses from our volunteers through a questionnaire composed both of open-ended and closed questions. Our results revealed some primary tasks which require cognitive support, including monitoring of pending and executed unit testing tasks, and navigating across unit testing related artifacts. We summarize our results in a framework, and based on it, we develop a research agenda as an actionable instrument to the community. Our study’s contributions comprise practical improvement suggestions for the current tools and describe further opportunities for research in software testing. Moreover, we comprehensively explain our qualitative methods.

On the challenges of open-sourcing proprietary software projects

The open source software (OSS) movement has become widely recognized as an effective way to deliver software. Even big software companies, well-known for being restrictive when it comes to publishing their source code artifacts, have recently adopted open source initiatives and released for general use the source code of some of their most notable products. We conducted an exploratory study on merits of the widespread belief that open-sourcing a proprietary software project will attract external developers, like casual contributors, and therefore improve software quality (e.g., “given enough eyeballs, all bugs are shallow”). By examining the pre- and post-migration software history of eight active, popular, non-trivial proprietary projects that became open source, we characterize the phenomenon and identify some challenges. Contrary to what many believe, we found that only a few projects experienced a growth in newcomers, contributions, and popularity; furthermore, this growth does not last long. The results from the study can be useful for helping software companies to better understand the hidden challenges of open-sourcing their software projects to attract external developers.

Empirical analysis of change metrics for software fault prediction

A quality assurance activity, known as software fault prediction, can reduce development costs and improve software quality. The objective of this study is to investigate change metrics in conjunction with code metrics to improve the performance of fault prediction models. Experimental studies are performed on different versions of Eclipse projects and change metrics are extracted from the GIT repositories. In addition to the existing change metrics, several new change metrics are defined and collected from the Eclipse project repository. Machine learning algorithms are applied in conjunction with the change and source code metrics to build fault prediction models. The classification model with new change metrics performs better than the models using existing change metrics. In this work, the experimental results demonstrate that change metrics have a positive impact on the performance of fault prediction models, and high-performance models can be built with several change metrics.

Software defect prediction techniques using metrics based on neural network classifier

Software industries strive for software quality improvement by consistent bug prediction, bug removal and prediction of fault-prone module. This area has attracted researchers due to its significant involvement in software industries. Various techniques have been presented for software defect prediction. Recent researches have recommended data-mining using machine learning as an important paradigm for software bug prediction. state-of-art software defect prediction task suffer from various issues such as classification accuracy. However, software defect datasets are imbalanced in nature and known fault prone due to its huge dimension. To address this issue, here we present a combined approach for software defect prediction and prediction of software bugs. Proposed approach delivers a concept of feature reduction and artificial intelligence where feature reduction is carried out by well-known principle component analysis (PCA) scheme which is further improved by incorporating maximum-likelihood estimation for error reduction in PCA data reconstruction. Finally, neural network based classification technique is applied which shows prediction results. A framework is formulated and implemented on NASA software dataset where four datasets i.e., KC1, PC3, PC4 and JM1 are considered for performance analysis using MATLAB simulation tool. An extensive experimental study is performed where confusion, precision, recall, classification accuracy etc. parameters are computed and compared with existing software defect prediction techniques. Experimental study shows that proposed approach can provide better performance for software defect prediction.

BRCode: An interpretive model-driven engineering approach for enterprise applications

Many model-driven engineering (MDE) approaches have been proposed in recent studies. They claim to improve software quality and productivity by raising the abstraction level at which developers work. However, they often fall short of what was expected in terms of productivity, profitability, and Return on Investment in real-world scenarios. This article proposes BRCode, which is an interpretive MDE approach for fast-changing enterprise applications. A case study that involves the development of an Enterprise Resource Planning (ERP) system enabled data collection based on 34 realistic scenarios in a Brazilian company. This evaluation compared BRCode with a generative MDE (genMDE) approach. Our results show that (1) genMDE required 93.75% more effort; and (2) genMDE and BRCode led to financial gains in 48% and 70% of the cases, respectively. On average, genMDE led to financial losses in most cases, while BRCode roughly tripled financial gains; (3) BRCode had an ROI of 1.54, compared to 0.07 for genMDE, which represents a difference of 93.37%. The results were encouraging and show the potential for using BRCode to support software production companies in the turbulent business environment.

Model for Automation of Software Testing using Genetic Algorithm to improve Software Quality

Model for Automation of Software Testing using Genetic Algorithm to improve Software Quality

Statistical Analysis of Metrics for Software Quality Improvement

Software product quality can be defined as the features and characteristics of the product that meet the user needs. The quality of any software can be achieved by following a well defined software process. These software process results into various metrics like Project metrics, Product metrics and Process metrics. Software quality depends on the process which is carried out to design and develop software. Even though the process can be carried out with utmost care, still it can introduce some error and defects. Process metrics are very useful from management point of view. Process metrics can be used for improving the software development and maintenance process for defect removal and also for reducing the response time. This paper describes the importance of capturing the Process metrics during the quality audit process and also attempts to categorize them based on the nature of error captured. To reduce such errors and defects found, steps for corrective actions are recommended.

Software Fault Prediction Using Filtering Feature Selection in Cluster-Based Classification

The high accuracy of software fault prediction can help testing effort and improving software quality. Previous researchers had proposed the combination of Entropy-Based Discretization (EBD) and Cluster-Based Classification (CBC). However, the irrelevant and redundant features in software fault dataset tend to decrease the prediction accuracy value. This study proposes improvement of CBC outcomes by integrating filtering feature selection methods. Filtering feature selection methods that will be integrated with CBC i.e. Information Gain (IG), Gain Ratio (GR), and One-R (OR). Based on the research using 2 datasets NASA public MDP (i.e. PC2 and PC3), the result shows that the combination of CBC and IG yields the best average accuracy value compared to GR and OR. It generates 67.52% average of probability detection (pd) and 37.42% average of probability false alarm (pf). While CBC without feature selection yields 65.38% average pd and 49.95% average pf. It can be concluded that IG can improve CBC outcomes by increasing 2.14% average pd and reducing 12.53% average pf

A Nonlinear Manifold Detection based Model for Software Defect Prediction

Software defect prediction requires developing a new technique which aims at accurately predict defective modules in software system with minimum time and space complexity as well as lesser computational cost. As such, a new model based on Nonlinear Manifold Detection Techniques has been proposed to eliminate undesirable and irrelevant attributes of high dimensional datasets by dimension reduction with more prediction accuracy and improved software quality. In this paper, a novel step towards achieving the goal by developing a new model based on Nonlinear MDTs and comparing its effectiveness with existing Feature Selection techniques for identifying the most accurate defect prediction method. The performance of different classification methods with both new and existing techniques has been evaluated, compared and also tested statistically by using Friedman test and Post Hoc analysis. The result proved that new proposed model based on Nonlinear MDTs is better performance oriented compared to accuracy level of all other techniques.

Recommending Refactoring Solutions Based on Traceability and Code Metrics

Software refactoring has been extensively used to rectify the design flaws and improve software quality without affecting its observable behaviors. For a given code smell, it is common that there exist multiple refactoring solutions. However, it is challenging for developers to select the best one from such potential solutions. Consequently, a number of approaches have been proposed to facilitate the selection. Such approaches compare and select among alternative refactoring solutions based on their impact on metrics of source code. However, their impact on the traceability between source code and requirements is ignored although the importance of such traceability has been well recognized. To this end, we select among alternative refactoring solutions according to how they improve the traceability as well as source code design. To quantify the quality of traceability and source code design we leverage the use of entropy-based and traditional coupling and cohesion metrics respectively. We virtually apply alternative refactoring solutions and measure their effect on the traceability and source code design. The one leading to greatest improvement is recommended. The proposed approach has been evaluated on a well-known data set. The evaluation results suggest that on up to 71% of the cases, developers prefer our recommendation to the traditional recommendation based on code metrics.

Defects Prediction and Prevention Approaches for Quality Software Development

The demand for distributed and complex business applications in the enterprise requires error-free and high-quality application systems. Unfortunately, most of the developed software contains certain defects which cause failure of a system. Such failures are unacceptable for the development in the critical or sensitive applications. This makes the development of high quality and defect free software extremely important in software development. It is important to better understand and compute the association among software defects and its failures for the effective prediction and elimination of these defects to decline the failure and improve software quality. This paper presents a review of software defects prediction and its prevention approaches for the quality software development. It also focuses a review on the potential and constraints of those mechanisms in quality product development and maintenance.

An empirical study to improve software quality through design patterns

Numerous elements influence the quality of software systems. One of these elements is the use of design patterns. This paper examines the influence of design patterns on quality requirements in particular maintainability. Improving the maintainability of software systems provides a higher opportunity for recovery because maintenance costs can use 60-80% of the complete budget of proprietorship of software systems. In addition, if the maintainability of the software system is poor, the destruction of the product will increase and the system will rapidly lose its importance to stakeholders. In this context, we propose a pattern guided approach to improve the maintainability of software products. Maintainability is measured by certain factors. We focus in this article on the observer and the facade design patterns to illustrate our work.

An empirical study to improve software quality through design patterns

An empirical study to improve software quality through design patterns

An effective method for improving software quality by automated test generation technique

The general undertaking of the product building is to guarantee conveyance of superb programming to the end client. To ensure great programming, it is required to test programming. Testing is a critical constituent of programming building. In programming testing there are number of fundamental issues like compelling age of experiments, prioritization of experiments which should be handled. This mechanized test structure predominantly relies upon these four angles: test system, experiment age, test execution and test assessment. Test methodology is an accumulation of systems that decides the testing way to deal with be trailed by the testing group. The experiment age alludes to the age of experiments in light of the given application. The test execution briefs about the execution of those tests at that point contrasting the normal outcome and genuine outcome. The test assessment explores the experiments and causes us to produce test rundown report and programming quality confirmation report consequently. The aim of delivering this device is to produce test cases naturally and to diminish the cost of testing notwithstanding collect the season of determining experiments physically. Subsequently this framework enhances general nature of the product.

As Dificuldades na Adoção e Uso de Método Scrum em Empresas Brasileiras Utilizando Processos Plan-Driven: Estudo de Caso Múltiplo

Companies have demonstrated growing interests about the adoption of agile methods in their software development processes, aiming to get the benefits related to productivity increasing, software quality improvement and a better way to handle requirements change, amongst others. However, the same companies already have consolidated processes, which get into conflict with values and practices established by the agile methods, hindering its adoption. This study presents the major difficulties in Scrum agile method adoption in organizations that use plan-driven processes. It was applied a multiple case study with three Brazilian organizations which use IT to support their business, with similar structure and process. After the result analysis, the major points of difficulty in Scrum adoption were identified and then consolidated in a way that allow companies to identify adjustment points in its processes to improve the adoption of Scrum.

Prediction of the Software Security Defects Based on the Complex Network

The traditional software defects prediction methods just evaluate the software defects based the unweighted undirected network, which do not reflect the real complex software system. Therefore, this paper proposes a software security defects prediction method based on the complex network. This method improves the PageRank algorithm and develops a KeyNodeRank algorithm based on the complex network. In this method, the software system is divided into different classes and these classes constitute a weighted directed network. In addition, the KeyNodeRank algorithm evaluates and ranks the importance of class nodes in the global network. In order to examine the validity of the software defects prediction method, this paper carries out an experiment. It is found that this method not only is accurate to predict and locate software defects and but also is very significant for improving software quality and maintaining software.