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Michael Felderer

Publications and source records attributed to Michael Felderer.

At least 73 records · Page 4Linked to original sources
arXiv2020

The Evolution of Empirical Methods in Software Engineering

Empirical methods like experimentation have become a powerful means to drive the field of software engineering by creating scientific evidence on software development, operation, and maintenance, but also by supporting practitioners in their decision making and learning. Today empirical methods are fully applied in software engineering. However, they have developed in several iterations since the 1960s. In this chapter we tell the history of empirical software engineering and present the evolution of empirical methods in software engineering in five iterations, i.e., (1) mid-1960s to mid-1970s, (2) mid-1970s to mid-1980s, (3) mid-1980s to end of the 1990s, (4) the 2000s, and (5) the 2010s. We present the five iterations of the development of empirical software engineering mainly from a methodological perspective and additionally take key papers, venues, and books, which are covered in chronological order in a separate section on recommended further readings, into account. We complement our presentation of the evolution of empirical software engineering by presenting the current situation and an outlook in Sect. 4 and the available books on empirical software engineering. Furthermore, based on the chapters covered in this book we discuss trends on contemporary empirical methods in software engineering related to the plurality of research methods, human factors, data collection and processing, aggregation and synthesis of evidence, and impact of software engineering research.

cs.SE↗
arXiv2020

Challenges in Survey Research

While being an important and often used research method, survey research has been less often discussed on a methodological level in empirical software engineering than other types of research. This chapter compiles a set of important and challenging issues in survey research based on experiences with several large-scale international surveys. The chapter covers theory building, sampling, invitation and follow-up, statistical as well as qualitative analysis of survey data and the usage of psychometrics in software engineering surveys.

cs.SE↗
arXiv2020

NLP-assisted software testing: A systematic mapping of the literature

Context: To reduce manual effort of extracting test cases from natural-language requirements, many approaches based on Natural Language Processing (NLP) have been proposed in the literature. Given the large amount of approaches in this area, and since many practitioners are eager to utilize such techniques, it is important to synthesize and provide an overview of the state-of-the-art in this area. Objective: Our objective is to summarize the state-of-the-art in NLP-assisted software testing which could benefit practitioners to potentially utilize those NLP-based techniques. Moreover, this can benefit researchers in providing an overview of the research landscape. Method: To address the above need, we conducted a survey in the form of a systematic literature mapping (classification). After compiling an initial pool of 95 papers, we conducted a systematic voting, and our final pool included 67 technical papers. Results: This review paper provides an overview of the contribution types presented in the papers, types of NLP approaches used to assist software testing, types of required input requirements, and a review of tool support in this area. Some key results we have detected are: (1) only four of the 38 tools (11%) presented in the papers are available for download; (2) a larger ratio of the papers (30 of 67) provided a shallow exposure to the NLP aspects (almost no details). Conclusion: This paper would benefit both practitioners and researchers by serving as an "index" to the body of knowledge in this area. The results could help practitioners utilizing the existing NLP-based techniques; this in turn reduces the cost of test-case design and decreases the amount of human resources spent on test activities. After sharing this review with some of our industrial collaborators, initial insights show that this review can indeed be useful and beneficial to practitioners.

cs.SE↗
arXiv2020

Risk Management Practices in Information Security: Exploring the Status Quo in the DACH Region

Information security management aims at ensuring proper protection of information values and information processing systems (i.e. assets). Information security risk management techniques are incorporated to deal with threats and vulnerabilities that impose risks to information security properties of these assets. This paper investigates the current state of risk management practices being used in information security management in the DACH region (Germany, Austria, Switzerland). We used an anonymous online survey targeting strategic and operative information security and risk managers and collected data from 26 organizations. We analyzed general practices, documentation artifacts, patterns of stakeholder collaboration as well as tool types and data sources used by enterprises to conduct information security management activities. Our findings show that the state of practice of information security risk management is in need of improvement. Current industrial practice heavily relies on manual data collection and complex potentially subjective decision processes with multiple stakeholders involved. Dedicated risk management tools and methods are used selectively and neglected in favor of general-purpose documentation tools and direct communication between stakeholders. In light of our results we propose guidelines for the development of risk management practices that are better aligned with the current operational situation in information security management.

cs.CY↗
arXiv2020

Software Engineering und Software Engineering Forschung im Zeitalter der Digitalisierung

Digitization not only affects society, it also requires a redefinition of the location of computer science and computer scientists, as the science journalist Yogeshwar suggests. Since all official aspects of digitalization are based on software, this article is intended to attempt to redefine the role of software engineering and its research. Software-based products, systems or services are influencing all areas of life and are a critical component and central innovation driver of digitization in all areas of life. Scientifically, there are new opportunities and challenges for software engineering as a driving discipline in the development of any technical innovation. However, the chances must not be sacrificed to the competition for bibliometric numbers as an end in themselves.

cs.SE↗
arXiv2019

A taxonomy of risk-based testing

Software testing has often to be done under severe pressure due to limited resources and a challenging time schedule facing the demand to assure the fulfillment of the software requirements. In addition, testing should unveil those software defects that harm the mission-critical functions of the software. Risk-based testing uses risk (re-)assessments to steer all phases of the test process in order to optimize testing efforts and limit risks of the software-based system. Due to its importance and high practical relevance several risk-based testing approaches were proposed in academia and industry. This paper presents a taxonomy of risk-based testing providing a framework to understand, categorize, assess, and compare risk-based testing approaches to support their selection and tailoring for specific purposes. The taxonomy is aligned with the consideration of risks in all phases of the test process and consists of the top-level classes risk drivers, risk assessment, and risk-based test process. The taxonomy of risk-based testing has been developed by analyzing the work presented in available publications on risk-based testing. Afterwards, it has been applied to the work on risk-based testing presented in this special section of the International Journal on Software Tools for Technology Transfer.

cs.SE↗
arXiv2019

Benefitting from the Grey Literature in Software Engineering Research

Researchers generally place the most trust in peer-reviewed, published information, such as journals and conference papers. By contrast, software engineering (SE) practitioners typically do not have the time, access or expertise to review and benefit from such publications. As a result, practitioners are more likely to turn to other sources of information that they trust, e.g., trade magazines, online blog-posts, survey results or technical reports, collectively referred to as Grey Literature (GL). Furthermore, practitioners also share their ideas and experiences as GL, which can serve as a valuable data source for research. While GL itself is not a new topic in SE, using, benefitting and synthesizing knowledge from the GL in SE is a contemporary topic in empirical SE research and we are seeing that researchers are increasingly benefitting from the knowledge available within GL. The goal of this chapter is to provide an overview to GL in SE, together with insights on how SE researchers can effectively use and benefit from the knowledge and evidence available in the vast amount of GL.

cs.SE↗
arXiv2019

Technical Debt and Waste in Non-Functional Requirements Documentation: An Exploratory Study

Background: To adequately attend to non-functional requirements (NFRs), they must be documented; otherwise, developers would not know about their existence. However, the documentation of NFRs may be subject to Technical Debt and Waste, as any other software artefact. Aims: The goal is to explore indicators of potential Technical Debt and Waste in NFRs documentation. Method: Based on a subset of data acquired from the most recent NaPiRE (Naming the Pain in Requirements Engineering) survey, we calculate, for a standard set of NFR types, how often respondents state they document a specific type of NFR when they also state that it is important. This allows us to quantify the occurrence of potential Technical Debt and Waste. Results: Based on 398 survey responses, four NFR types (Maintainability, Reliability, Usability, and Performance) are labelled as important but they are not documented by more than 22% of the respondents. We interpret that these NFR types have a higher risk of Technical Debt than other NFR types. Regarding Waste, 15% of the respondents state they document NFRs related to Security and they do not consider it important. Conclusions: There is a clear indication that there is a risk of Technical Debt for a fixed set of NFRs since there is a lack of documentation of important NFRs. The potential risk of incurring Waste is also present but to a lesser extent.

cs.SE↗
arXiv2019

Do We Preach What We Practice? Investigating the Practical Relevance of Requirements Engineering Syllabi - The IREB Case

Nowadays, there exist a plethora of different educational syllabi for Requirements Engineering (RE), all aiming at incorporating practically relevant educational units (EUs). Many of these syllabi are based, in one way or the other, on the syllabi provided by the International Requirements Engineering Board (IREB), a non-profit organisation devoted to standardised certification programs for RE. IREB syllabi are developed by RE experts and are, thus, based on the assumption that they address topics of practical relevance. However, little is known about to what extent practitioners actually perceive those contents as useful. We have started a study to investigate the relevance of the EUs included in the IREB Foundation Level certification programme. In a first phase reported in this paper, we have surveyed practitioners mainly from DACH countries (Germany, Austria and Switzerland) participating in the IREB certification. Later phases will widen the scope both by including other countries and by not requiring IREB-certified participants. The results shall foster a critical reflection on the practical relevance of EUs built upon the de-facto standard syllabus of IREB.

cs.SE↗
arXiv2019

Technical Debt in Data-Intensive Software Systems

The ever-increasing amount, variety as well as generation and processing speed of today's data pose a variety of new challenges for developing Data-Intensive Software Systems (DISS). As with developing other kinds of software systems, developing DISS is often done under severe pressure and strict schedules. Thus, developers of DISS often have to make technical compromises to meet business concerns. This position paper proposes a conceptual model that outlines where Technical Debt (TD) can emerge and proliferate within such data-centric systems by separating a DISS into three parts (Software Systems, Data Storage Systems and Data). Further, the paper illustrates the proliferation of Database Schema Smells as TD items within a relational database-centric software system based on two examples.

cs.SE↗
arXiv2019

A Taxonomy to Assess and Tailor Risk-based Testing in Recent Testing Standards

This article provides a taxonomy for risk-based testing that serves as a tool to define, tailor, or assess risk-based testing approaches in general and to instantiate risk-based testing approaches for the current testing standards ISO/IEC/IEEE 29119, ETSI EG and OWASP Security Testing Guide in particular. We demonstrate the usefulness of the taxonomy by applying it to the aforementioned standards as well as to the risk-based testing approaches SmartTesting, RACOMAT, PRISMA and risk-based test case prioritization using fuzzy expert systems. In this setting, the taxonomy is used to systematically identify deviations between the standards' requirements and the individual testing approaches so that we are able to position and compare the testing approaches and discuss their potential for practical application.

cs.SE↗
arXiv2019

Specification-Driven Predictive Business Process Monitoring

Predictive analysis in business process monitoring aims at forecasting the future information of a running business process. The prediction is typically made based on the model extracted from historical process execution logs (event logs). In practice, different business domains might require different kinds of predictions. Hence, it is important to have a means for properly specifying the desired prediction tasks, and a mechanism to deal with these various prediction tasks. Although there have been many studies in this area, they mostly focus on a specific prediction task. This work introduces a language for specifying the desired prediction tasks, and this language allows us to express various kinds of prediction tasks. This work also presents a mechanism for automatically creating the corresponding prediction model based on the given specification. Differently from previous studies, instead of focusing on a particular prediction task, we present an approach to deal with various prediction tasks based on the given specification of the desired prediction tasks. We also provide an implementation of the approach which is used to conduct experiments using real-life event logs.

cs.AI↗
arXiv2019

On Testing Data-Intensive Software Systems

Today's software systems like cyber-physical production systems or big data systems have to process large volumes and diverse types of data which heavily influences the quality of these so-called data-intensive systems. However, traditional software testing approaches rather focus on functional behavior than on data aspects. Therefore, the role of data in testing has to be rethought and specific testing approaches for data-intensive software systems are required. Thus, the aim of this chapter is to contribute to this area by (1) providing basic terminology and background on data-intensive software systems and their testing, and (2) presenting the state of the research and the hot topics in the area. Finally, the directions of research and the new frontiers on testing data-intensive software systems are discussed.

cs.SE↗
arXiv2018

Status Quo in Requirements Engineering: A Theory and a Global Family of Surveys

Requirements Engineering (RE) has established itself as a software engineering discipline during the past decades. While researchers have been investigating the RE discipline with a plethora of empirical studies, attempts to systematically derive an empirically-based theory in context of the RE discipline have just recently been started. However, such a theory is needed if we are to define and motivate guidance in performing high quality RE research and practice. We aim at providing an empirical and valid foundation for a theory of RE, which helps software engineers establish effective and efficient RE processes. We designed a survey instrument and theory that has now been replicated in 10 countries world-wide. We evaluate the propositions of the theory with bootstrapped confidence intervals and derive potential explanations for the propositions. We report on the underlying theory and the full results obtained from the replication studies with participants from 228 organisations. Our results represent a substantial step forward towards developing an empirically-based theory of RE giving insights into current practices with RE processes. The results reveal, for example, that there are no strong differences between organisations in different countries and regions, that interviews, facilitated meetings and prototyping are the most used elicitation techniques, that requirements are often documented textually, that traces between requirements and code or design documents is common, requirements specifications themselves are rarely changed and that requirements engineering (process) improvement endeavours are mostly intrinsically motivated. Our study establishes a theory that can be used as starting point for many further studies for more detailed investigations. Practitioners can use the results as theory-supported guidance on selecting suitable RE methods and techniques.

cs.SE↗
arXiv2018

A survey on software testability

Context: Software testability is the degree to which a software system or a unit under test supports its own testing. To predict and improve software testability, a large number of techniques and metrics have been proposed by both practitioners and researchers in the last several decades. Reviewing and getting an overview of the entire state-of-the-art and state-of-the-practice in this area is often challenging for a practitioner or a new researcher. Objective: Our objective is to summarize the body of knowledge in this area and to benefit the readers (both practitioners and researchers) in preparing, measuring and improving software testability. Method: To address the above need, the authors conducted a survey in the form of a systematic literature mapping (classification) to find out what we as a community know about this topic. After compiling an initial pool of 303 papers, and applying a set of inclusion/exclusion criteria, our final pool included 208 papers. Results: The area of software testability has been comprehensively studied by researchers and practitioners. Approaches for measurement of testability and improvement of testability are the most-frequently addressed in the papers. The two most often mentioned factors affecting testability are observability and controllability. Common ways to improve testability are testability transformation, improving observability, adding assertions, and improving controllability. Conclusion: This paper serves for both researchers and practitioners as an "index" to the vast body of knowledge in the area of testability. The results could help practitioners measure and improve software testability in their projects.

cs.SE↗
arXiv2018

Closing the gap between software engineering education and industrial needs

According to different reports, many recent software engineering graduates often face difficulties when beginning their professional careers, due to misalignment of the skills learnt in their university education with what is needed in industry. To address that need, many studies have been conducted to align software engineering education with industry needs. To synthesize that body of knowledge, we present in this paper a systematic literature review (SLR) which summarizes the findings of 33 studies in this area. By doing a meta-analysis of all those studies and using data from 12 countries and over 4,000 data points, this study will enable educators and hiring managers to adapt their education / hiring efforts to best prepare the software engineering workforce.

cs.SE↗
arXiv2018

Verlässliche Software im 21. Jahrhundert

Software is the main innovation driver in many different areas, like cloud services, autonomous driving, connected medical devices, and high-frequency trading. All these areas have in common that they require high dependability. In this paper, we discuss challenges and research directions imposed by these new areas on guaranteeing the dependability. On the one hand challenges include characteristics of the systems themselves, e. g., open systems and ad-hoc structures. On the other hand, we see new aspects of dependability like behavioral traceability.

cs.SE↗