Automated Static Analysis Tools: A Multidimensional view on Software Quality Evolution

Leveraging and acting on the results of static analysis is a well-known best practice. Static analysis also is an integral part of software quality assurance which is highlighted by the inclusion of static analysis results in software quality models like ColumbusQM and Quamoco. Although there are studies that explore if tools for static analysis are used and how they are configured, few publications explore the longitudinal effects of acting on static analysis results on software and on the evolution of software. Especially effects on quality criteria, e.g., software quality metrics, defects, or readability are missing. With our research, we will bridge this gap and measure the effects of static analysis on software quality evolution. We will measure the effect the removal of code that generates static analysis warnings has on software quality metrics. Furthermore, we will measure long term effects on external quality attributes, e.g., reported issues and defects. Finally, we want to predict false positives of static analysis warnings by training predictive models on our collected data.

Estimating the quality of software modules provides a practical way to assess their potential risk. A software quality estimation can be used to provide a targeted software quality improvement initiative. The aim is to provide the management team with a practical quality assessment of software modules for a cost‐effective utilization of software quality improvement resources. In our previous and ongoing research, we have empirically investigated three different kinds of software quality models that are built using software measurement data—software quality classification, software fault prediction, and module‐order modeling. The classification model predicts the class membership of software modules into the fault‐prone and not fault‐prone classes. The fault prediction model estimates the number of faults expected in the modules. A module‐order model predicts the ranking of the modules with respect to their relative software quality. The preference among the three software quality models depends on the software quality improvement goals of the given software project team. We present the useful principles and a detailed overview of the three software quality estimation models, using a case study of a large‐scale telecommunications software system. The classification model is built using the logistic regression technique, whereas the fault prediction model is built using the multiple linear regression technique. The module‐order model is built using the estimations of the fault prediction model for ranking the software modules. We summarize our work with suggestions for software tools that can be used to build software quality estimation models.

Empirical Analysis of Quality Models in Practice in Small IT Companies in SEE Region

Many previous research studies on software quality enhancement have only focused on the technical aspects of software quality, such as reliability, maintainability and functionality. However, the nature of enterprise software requires additional focus on transferring and integration of customer knowledge, for customization, enhancement, maintenance and training. As customers are seen as one of the most important stakeholders in software projects, it would appear that the current use of customer knowledge in software development is insufficient. This study concentrates on investigating Human, Organizational and Technological factors of customer knowledge management (CKM) in order to enhance the quality of enterprise software within software companies. After investigating CKM factors in the literature, a Technique for the Order of Preference by Similarity to Ideal Solution (TOPSIS) was used to rank these factors regarding the importance of CKM development in enterprise software companies, in order to improve software product quality. The weight and priority of the factors were determined by 31 software development company experts. Based on the highest priority factors, a theoretical model was developed. This study proposes a fundamental model that can be used as a guideline for successful CKM applications, within enterprise software development companies, to improve software quality.

In machine learning the problem of limited data for supervised learning is a challenging problem with practical applications. We address a similar problem in the context of software quality modeling. Knowledge- based software engineering includes the use of quantitative software quality estimation models. Such models are trained using apriori software quality knowledge in the form of software metrics and defect data of previously developed software projects. However, various practical issues limit the availability of defect data for all modules in the training data. We present two solutions to the problem of software quality modeling when a limited number of training modules have known defect data. The proposed solutions are a semisupervised clustering with expert input scheme and a semisupervised classification approach with the expectation-maximization algorithm. Software measurement datasets obtained from multiple NASA software projects are used in our empirical investigation. The software quality knowledge learnt during the semisupervised learning processes provided good generalization performances for multiple test datasets. In addition, both solutions provided better predictions compared to a supervised learner trained on the initial labeled dataset.

Agile scrum projects are gaining popularity for software development and delivery, due to its inherent attributes of flexibility in execution and quicker time to market. However, the critics of Agile have been raising questions on the predictability of success of Agile execution as the emphasis given to quantitative management using predictive techniques for an Agile project may not be to the extent of large mission critical projects which uses in-process leading indicators to predict outcomes. One possible solution to this challenge is to use the best practices from industry adopted models like the Capability Maturity Model Integration® for planning and managing Agile software delivery. In this paper, we present a study were a prediction model was developed for use in Agile projects for systematic in-process monitoring and decision making. The purpose of this paper is to propose a predictive model as a driver for continuous improvement of Delivered Defect Density in the context of a Scrum project.Our findings show that using the prediction model, the success of Agile project could be forecasted and controlled during its planning and execution phases as against waiting for the final release to The paper starts with defining the background and context of the study, research methodology and study of existing literature in this area. A brief description of the major knowledge areas considered in the study namely Agile, CMMI and High Maturity practices in CMMI is then presented. An introduction on measures, metrics and prediction models is also provided. The development of the Agile Prediction Model and the application of the model Agile SCRUM projects is addressed as the main theme of the paper which concludes with the advantages, shortcomings and limitations of the model developed. 1. Background of the study In organizations where quality of software is a primary goal for the software projects, it is useful to measure and observe Delivered Defect Density as a lagging indicator signifying the quality of developed software. Though this is a good metric to indicate the goodness of the delivered software, the metric lacks the ability to be controlled, as it is an outcome of the software development process and not an in-process metric. In this context, the need for in-process or leading indicators which will help practitioners get visibility on the outcome is important. In this paper, the processes and sub-processes leading to Delivered Defect Density is

Ontology Software Quality Model for Fuzzy Logic Evaluation Approach

Machine Learning (ML) projects incur novel challenges in their development and productionisation over traditional software applications, though established principles and best practices in ensuring the project's software quality still apply. While using static analysis to catch code smells has been shown to improve software quality attributes, it is only a small piece of the software quality puzzle, especially in the case of ML projects given their additional challenges and lower degree of Software Engineering (SE) experience in the data scientists that develop them. We introduce the novel concept of project smells which consider deficits in project management as a more holistic perspective on software quality in ML projects. An open-source static analysis tool mllint was also implemented to help detect and mitigate these. Our research evaluates this novel concept of project smells in the industrial context of ING, a global bank and large software- and data-intensive organisation. We also investigate the perceived importance of these project smells for proof-of-concept versus production-ready ML projects, as well as the perceived obstructions and benefits to using static analysis tools such as mllint. Our findings indicate a need for context-aware static analysis tools, that fit the needs of the project at its current stage of development, while requiring minimal configuration effort from the user.

Cost, schedule and quality are highly correlated factors in software development. They basically form three sides of the same triangle. Beyond a certain point (the "Quality is Free" point), it is difficult to increase the quality without increasing either cost or schedule or both for the software under development. As products and applications mature, users expect higher quality products. They want IT organizations to be responsible and accountable for the quality claims made by the product marketing teams. In the last couple decades, much software engineering research has focussed on standards, methodologies and techniques for improving software quality, measuring software quality and software quality assurance. Most of this research is focused on the internal/development view of quality. More recent studies done in conjunction with the marketing groups have made attempts to understand the customer view of quality. All of these different ongoing activities to understand quality from the various perspectives have made the field even more enriching and exciting. The Fifth Workshop on Software Quality aims to bring together academic, industrial and commercial communities interested in software quality topics to discuss the different technologies being defined and used in the software quality area.

This article describes empirical research results regarding the "history effect" in software quality evaluation processes. Most software quality models and evaluation processes models assume that software quality may be deterministically evaluated, especially when it is evaluated by experts. Consequently, software developers focus on the technical characteristics of the software product. A similar assumption is common in most engineering disciplines. However, in regard to other kinds of goods, direct violations of the assumption about objective evaluation were shown to be affected by the consequences of cognitive processes limitations. Ongoing discussion in the area of behavioral economics raises the question: are the experts prone to observation biases? If they are, then software quality models overlook an important aspect of software quality evaluation. This article proposes an experiment that aims to trace the influence of users' knowledge on software quality assessment. Measuring the influence of single variables for the software quality perception process is a complex task. There is no valid quality model for the precise measurement of product quality, and consequently software engineering does not have tools to freely manipulate the quality level for a product. This article proposes a simplified method to manipulate the observed quality level, thereby making it possible to conduct research. The proposed experiment has been conducted among professional software evaluators. The results show the significant negative influence (large effect size) of negative experience of users on final opinion about software quality regardless of its actual level.

Software quality is considered as one of the most highly interacting aspects in software engineering. It has many dimensions that vary depending on the users' requirements and their points of view. Thus, the varying dimensions lead to complications in measuring and defining the software quality appropriately. The use of libraries increases software quality more than using generic programming as these libraries are prepared and tested in advance. Moreover, these libraries reduce the effort spent in the designing, testing and the maintaining processes. In this research, a new model is introduced to calculate the saved effort that results from using libraries instead of generic programming in testing, coding, and productivity processes. The proposed model consists of three metrics. These metrics are the library investment ratio, the library investment level, and program simplicity. An experimental analysis has been done onto ten software products to compare the outcomes of the model with reuse percent. The outcomes show that the model gives better results than reuse percent, because the model is deepening in the source code more than the reuse percent does. Also, the model has a better effect on the improvement of software quality and productivity, rather than reuse percent.

The research topic focuses on the characterization of software quality considering the main software elements such as people, process and product. Many attributes (size, language, testing techniques etc.) probably could have an effect on the quality of software. In this thesis we aim to understand the impact of attributes of three P's (people, product, process) on the quality of software by empirical means. Software quality can be interpreted in many ways, such as customer satisfaction, stability and defects etc. In this thesis we adopt ‘defect density' as a quality measure. Therefore the research focus on the empirical evidences of the impact of attributes of the three P's on the software defect density. For this reason empirical research methods (systematic literature reviews, case studies, and interviews) are utilized to collect empirical evidence. Each of this research method helps to extract the empirical evidences of the object under study and for data analysis statistical methods are used. Considering the product attributes, we have studied the size, language, development mode, age, complexity, module structure, module dependency, and module quality and their impact on project quality. Considering the process attributes, we have studied the process maturity and structure, and their impact on the project quality. Considering the people attributes, we have studied the experience and capability, and their impact on the project quality. Moreover, in the process category, we have studied the impact of one testing approach called ‘exploratory testing' and its impact on the quality of software. Exploratory testing is a widely used software-testing practice and means simultaneous learning, test design, and test execution. We have analyzed the exploratory testing weaknesses, and proposed a hybrid testing approach in an attempt to improve the quality. Concerning the product attributes, we found that there exist a significant difference of quality between open and close source projects, java and C projects, and large and small projects. Very small and defect free modules have impact on the software quality. Different complexity metrics have different impact on the software quality considering the size. Product complexity as defined in Table 53 has partial impact on the software quality. However software age and module dependencies are not factor to characterize the software quality. Concerning the people attributes, we found that platform experience, application experience and language and tool experience have significant impact on the software quality. Regarding the capability we found that programmer capability has partial impact on the software quality where as analyst capability has no impact on the software quality. Concerning process attributes we found that there is no difference of quality between the project developed under CMMI and those that are not developed under CMMI. Regarding the CMMI levels there is difference of software quality particularly between CMMI level 1 and CMMI level 3. Comparing different process types we found that hybrid projects are of better quality than waterfall projects. Process maturity defined by (SEI-CMM) has partial impact on the software quality. Concerning exploratory testing, we found that exploratory testing weaknesses induce the testing technical debt therefore a process is defined in conjunction with the scripted testing in an attempt to reduce the associated technical debt of exploratory testing. The findings are useful for both researchers and practitioners to evaluate their projects

Current software quality estimation models often involve the use of supervised learning methods for building a software fault prediction models. In such models, dependent variable usually represents a software quality measurement indicating the quality of a module by risk-basked class membership, or the number of faults. Independent variables include various software metrics as McCabe, Error Count, Halstead, Line of Code, etc... In this paper we present the use of advanced tool for data mining called Multimethod on the case of building software fault prediction model. Multimethod combines different aspects of supervised learning methods in dynamical environment and therefore can improve accuracy of generated prediction model. We demonstrate the use Multimethod tool on the real data from the Metrics Data Project Data (MDP) Repository. Our preliminary empirical results show promising potentials of this approach in predicting software quality in a software measurement and quality dataset.

Software Quality Assurance is a sub-process that ensures that developed software meets and complies with defined or standardized quality specifications. Focusing on source code, there are characteristics that can be used to evaluate the quality. Introductory courses must encourage freshmen students to improve internal quality of their source code, but only as sophomore they have contact with Software Engineering concepts, including Quality Assurance. In this paper we present a tool to source code quality evaluation aimed at supporting students to improve their source code and, consequently, their programming skills. The proposed tool uses quality reports (available to professional environment integrate with software repositories) to analyze students' source code and provide a feedback about the student coding. The proposed tool run locally, with few computational resources. In addition, we proposed the methodology to use the proposed tool: it consists of challenging students to perform a set of maintenance tasks in a controlled environment. We prepared a source code by introducing common defects, what decreases the quality of source code, and ask to students to perform maintenance tasks in order to both eliminate the introduced defects and introduce new features. After each modification, the students must evaluate their code using the proposed tool to obtain a feedback about quality of source code. To evaluate the approach and the tool, we created a survey and applied to students and the teacher. As a result, we show the benefits of using the proposed tool to both teachers and students perspectives. The results are positive to enhance the teaching-learning Software Quality Assurance to Software Engineering students.

Software evolution is an essential foundation in delivering technology that adapts to user needs and industry dynamics. In an era of rapid technological development, software evolution is not just a necessity, but a must to ensure long-term relevance. Developers are faced with major challenges in maintaining and improving software quality over time. This research aims to investigate the correlation between feature volatility and bug occurrence rate in software evolution, to understand the impact of dynamic feature changes on software quality and development process. The research method uses commit analysis on the dataset as a marker of bug presence, studying the complex relationship between feature volatility and bug occurrence rate to reveal the interplay in software development. Validated datasets are measured by metrics and correlations are measured by Pearson-product-moment analysis. This research found a strong relationship between feature volatility and bug occurrence rate, suggesting that an increase in feature changes correlates with an increase in bugs that impact software stability and quality. This research provides important insights into the correlation between feature volatility and bug occurrence rates, guiding developers and quality practitioners to develop more effective testing strategies in dynamic development environments.