EMPIRICAL CHARACTERIZATION OF SOFTWARE QUALITY

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 project

Software Engineering Laboratory Series: Collected Software Engineering Papers

The Software Engineering Laboratory (SEL) is an organization sponsored by NASA/GSFC and created to investigate the effectiveness of software engineering technologies when applied to the development of application software. The activities, findings, and recommendations of the SEL are recorded in the Software Engineering Laboratory Series, a continuing series of reports that includes this document

A Comprehensive Analysis of Literature Reported Software Engineering Advancements Using AHP

The paper provides a various potential improvements in software engineering using analytic hierarchical processing (AHP). The presented work could support in assessing the selection of process, project, methods and tools depending on various situations encounter during software engineering. AHP belongs to Multi Criteria Decision making methods which seems to be a continuous research to solve critical and complex scientific and software engineering applications. This paper discusses existing key research contributions and their advancements in the areas of both software engineering and in combination of AHP with software engineering

EMPIRICAL ASSESSMENT OF THE IMPACT OF USING AUTOMATIC STATIC ANALYSIS ON CODE QUALITY

Automatic static analysis (ASA) tools analyze the source or compiled code looking for violations of recommended programming practices (called issues) that might cause faults or might degrade some dimensions of software quality. Antonio Vetro' has focused his PhD in studying how applying ASA impacts software quality, taking as reference point the different quality dimensions specified by the standard ISO/IEC 25010. The epistemological approach he used is that one of empirical software engineering. During his three years PhD, he's been conducting experiments and case studies on three main areas: Functionality/Reliability, Performance and Maintainability. He empirically proved that specific ASA issues had impact on these quality characteristics in the contexts under study: thus, removing them from the code resulted in a quality improvement. Vetro' has also investigated and proposed new research directions for this field: using ASA to improve software energy efficiency and to detect the problems deriving from the interaction of multiple languages. The contribution is enriched with the final recommendation of a generalized process for researchers and practitioners with a twofold goal: improve software quality through ASA and create a body of knowledge on the impact of using ASA on specific software quality dimensions, based on empirical evidence. This thesis represents a first step towards this goa

Framework for the Automation of SDLC Phases using Artificial Intelligence and Machine Learning Techniques

Software Engineering acts as a foundation stone for any software that is being built. It provides a common road-map for construction of software from any domain. Not following a well-defined Software Development Model have led to the failure of many software projects in the past. Agile is the Software Development Life Cycle (SDLC) Model that is widely used in practice in the IT industries to develop software on various technologies such as Big Data, Machine Learning, Artificial Intelligence, Deep learning. The focus on Software Engineering side in the recent years has been on trying to automate the various phases of SDLC namely- Requirements Analysis, Design, Coding, Testing and Operations and Maintenance. Incorporating latest trending technologies such as Machine Learning and Artificial Intelligence into various phases of SDLC, could facilitate for better execution of each of these phases. This in turn helps to cut-down costs, save time, improve the efficiency and reduce the manual effort required for each of these phases. The aim of this paper is to present a framework for the application of various Artificial Intelligence and Machine Learning techniques in the different phases of SDLC

Technical Debt Prioritization: State of the Art. A Systematic Literature Review

Background. Software companies need to manage and refactor Technical Debt issues. Therefore, it is necessary to understand if and when refactoring Technical Debt should be prioritized with respect to developing features or fixing bugs. Objective. The goal of this study is to investigate the existing body of knowledge in software engineering to understand what Technical Debt prioritization approaches have been proposed in research and industry. Method. We conducted a Systematic Literature Review among 384 unique papers published until 2018, following a consolidated methodology applied in Software Engineering. We included 38 primary studies. Results. Different approaches have been proposed for Technical Debt prioritization, all having different goals and optimizing on different criteria. The proposed measures capture only a small part of the plethora of factors used to prioritize Technical Debt qualitatively in practice. We report an impact map of such factors. However, there is a lack of empirical and validated set of tools. Conclusion. We observed that technical Debt prioritization research is preliminary and there is no consensus on what are the important factors and how to measure them. Consequently, we cannot consider current research conclusive and in this paper, we outline different directions for necessary future investigations

Is "Better Data" Better than "Better Data Miners"? (On the Benefits of Tuning SMOTE for Defect Prediction)

We report and fix an important systematic error in prior studies that ranked classifiers for software analytics. Those studies did not (a) assess classifiers on multiple criteria and they did not (b) study how variations in the data affect the results. Hence, this paper applies (a) multi-criteria tests while (b) fixing the weaker regions of the training data (using SMOTUNED, which is a self-tuning version of SMOTE). This approach leads to dramatically large increases in software defect predictions. When applied in a 5*5 cross-validation study for 3,681 JAVA classes (containing over a million lines of code) from open source systems, SMOTUNED increased AUC and recall by 60% and 20% respectively. These improvements are independent of the classifier used to predict for quality. Same kind of pattern (improvement) was observed when a comparative analysis of SMOTE and SMOTUNED was done against the most recent class imbalance technique. In conclusion, for software analytic tasks like defect prediction, (1) data pre-processing can be more important than classifier choice, (2) ranking studies are incomplete without such pre-processing, and (3) SMOTUNED is a promising candidate for pre-processing.Comment: 10 pages + 2 references. Accepted to International Conference of Software Engineering (ICSE), 201

Quality-Aware Learning to Prioritize Test Cases

Software applications evolve at a rapid rate because of continuous functionality extensions, changes in requirements, optimization of code, and fixes of faults. Moreover, modern software is often composed of components engineered with different programming languages by different internal or external teams. During this evolution, it is crucial to continuously detect unintentionally injected faults and continuously release new features. Software testing aims at reducing this risk by running a certain suite of test cases regularly or at each change of the source code. However, the large number of test cases makes it infeasible to run all test cases. Automated test case prioritization and selection techniques have been studied in order to reduce the cost and improve the efficiency of testing tasks. However, the current state-of-art techniques remain limited in some aspects. First, the existing test prioritization and selection techniques often assume that faults are equally distributed across the software components, which can lead to spending most of the testing budget on components less likely to fail rather than the ones highly to contain faults. Second, the existing techniques share a scalability problem not only in terms of the size of the selected test suite but also in terms of the round-trip time between code commits and engineer feedback on test cases failures in the context of Continuous Integration (CI) development environments. Finally, it is hard to algorithmically capture the domain knowledge of the human testers which is crucial in testing and release cycles. This thesis is a new take on the old problem of reducing the cost of software testing in these regards by presenting a data-driven lightweight approach for test case prioritization and execution scheduling that is being used (i) during CI cycles for quick and resource-optimal feedback to engineers, and (ii) during release planning by capturing the testers domain knowledge and release requirements. Our approach combines software quality metrics with code churn metrics to build a regressive model that predicts the fault density of each component and a classification model to discriminate faulty from non-faulty components. Both models are used to guide the testing effort to the components likely to contain the largest number of faults. The predictive models have been validated on eight industrial automotive software applications at Daimler, showing a classification accuracy of 89% and an accuracy of 85.7% for the regression model. The thesis develops a test cases prioritization model based on features of the code change, the tests execution history and the component development history. The model reduces the cost of CI by predicting whether a particular code change should trigger the individual test suites and their corresponding test cases. In order to algorithmically capture the domain knowledge and the preferences of the tester, our approach developed a test case execution scheduling model that consumes the testers preferences in the form of a probabilistic graph and solves the optimal test budget allocation problem both online in the context of CI cycles and offline when planning a release. Finally, the thesis presents a theoretical cost model that describes when our prioritization and scheduling approach is worthwhile. The overall approach is validated on two industrial analytical applications in the area of energy management and predictive maintenance, showing that over 95% of the test failures are still reported back to the engineers while only 43% of the total available test cases are being executed