A Programming Environment Evaluation Methodology for Object-Oriented Systems

The object-oriented design strategy as both a problem decomposition and system development paradigm has made impressive inroads into the various areas of the computing sciences. Substantial development productivity improvements have been demonstrated in areas ranging from artificial intelligence to user interface design. However, there has been very little progress in the formal characterization of these productivity improvements and in the identification of the underlying cognitive mechanisms. The development and validation of models and metrics of this sort require large amounts of systematically-gathered structural and productivity data. There has, however, been a notable lack of systematically-gathered information on these development environments. A large part of this problem is attributable to the lack of a systematic programming environment evaluation methodology that is appropriate to the evaluation of object-oriented systems

An Investigation into the Imposed Cognitive Load of Static & Dynamic Type Systems on Programmers

Static and dynamic type systems have long been a point of contention in the programming language wars. Yet, for many years, arguments on either side were drawn from personal experience and not empirical evidence. A challenge for researchers is that the usability of language constructs is difficult to quantify, especially since usability can be interpreted in many ways. By one definition, language usability can be measured in terms of the level of cognitive load imposed on a developer. This can be done through questionnaires, but ultimately user responses are subject to bias. In recent years, eye-tracking has been shown to be an effective means of measuring cognitive load via direct physiological measures. Towards the goal of measuring type system usability, we present a user study in which participants completed programming tasks in Java and Groovy. This thesis explored the use of the Index of Cognitive Activity (ICA) as a cognitive load measurement tool and considered novices and experts separately in the analysis. We found ICA to be an ineffective means of measuring type system usability and we cannot say conclusively whether it can be generally applied to programming tasks. Despite this, our results contradict previous studies as we found type system did not affect success rate, task completion time, or perceived task difficulty

Business Process Innovation using the Process Innovation Laboratory

Most organizations today are required not only to establish effective business processes but they are required to accommodate for changing business conditions at an increasing rate. Many business processes extend beyond the boundary of the enterprise into the supply chain and the information infrastructure therefore is critical. Today nearly every business relies on their Enterprise System (ES) for process integration and the future generations of enterprise systems will increasingly be driven by business process models. Consequently process modeling and improvement will become vital for business process innovation (BPI) in future organizations. There is a significant body of knowledge on various aspect of process innovation, e.g. on conceptual modeling, business processes, supply chains and enterprise systems. Still an overall comprehensive and consistent theoretical framework with guidelines for practical applications has not been identified. The aim of this paper is to establish a conceptual framework for business process innovation in the supply chain based on advanced enterprise systems. The main approach to business process innovation in this context is to create a new methodology for exploring process models and patterns of applications. The paper thus presents a new concept for business process innovation called the process innovation laboratory a.k.a. the Ð-Lab. The Ð-Lab is a comprehensive framework for BPI using advanced enterprise systems. The Ð-Lab is a collaborative workspace for experimenting with process models and an explorative approach to study integrated modeling in a controlled environment. The Ð-Lab facilitates innovation by using an integrated action learning approach to process modeling including contemporary technological, organizational and business perspectivesNo; keywords

Giving RSEs a Larger Stage through the Better Scientific Software Fellowship

The Better Scientific Software Fellowship (BSSwF) was launched in 2018 to foster and promote practices, processes, and tools to improve developer productivity and software sustainability of scientific codes. BSSwF's vision is to grow the community with practitioners, leaders, mentors, and consultants to increase the visibility of scientific software production and sustainability. Over the last five years, many fellowship recipients and honorable mentions have identified as research software engineers (RSEs). This paper provides case studies from several of the program's participants to illustrate some of the diverse ways BSSwF has benefited both the RSE and scientific communities. In an environment where the contributions of RSEs are too often undervalued, we believe that programs such as BSSwF can be a valuable means to recognize and encourage community members to step outside of their regular commitments and expand on their work, collaborations and ideas for a larger audience.Comment: submitted to Computing in Science & Engineering (CiSE), Special Issue on the Future of Research Software Engineers in the U

Authoring Game-Based Adaptive Units of Learning with IMS Learning Design and <e-Adventure>

Burgos, D., Moreno-Ger, P., Sierra, J. L., Fernández Manjón, B., & Kooper, R. (2007). Authoring Game-Based Adaptive Units of Learning with IMS Learning Design and <e-Adventure>. International Journal of Learning Technology, 3(3), 252-268.Electronic games and simulations (eGames) are a valuable support for adaptive learning. This adaptation can be based on different inputs, such as the user´s performance, behaviour or cognitive load. Both adaptation and eGames can be modelled with IMS Learning Design or integrated from an external resource. In this article we show the relation between IMS Learning Design and the <e-Adventure> Project when it comes to authoring adaptive Units of Learning integrated with eGames. We first describe the challenges of this objective and the several different solutions on authoring and integration. We also describe the content-centered authoring approach in <e-Adventure>, and the need for a communication service with IMS LD that makes a bi-directional influence on the user’s adaptive learning experience. At the end, we describe a practical example that illustrates how an adaptive IMS LD Unit of Learning with an integrated <e-Adventure> eGame is developed.This paper is partially supported by the European projects TENCompetence (IST-TEL/2004-2.4.10, www.tencompetence.org) and ProLearn (IST 507310, www.prolearn-project.org), and the research group <e-Ucm> (www.e-ucm.es). The Education and Science Spanish Committee (projects MetaLearn TIN2004 08367 C02-02 and OdA Virtual TIN2005-08788-C04-01) and the Regional Government / Complutense University of Madrid (grant 4155/2005 and research group 910494) have also supported this work partially. Special thanks to Bruno Torijano Bueno for his participation in the creation and preliminary tests of the sample game

Evidence-driven testing and debugging of software systems

Program debugging is the process of testing, exposing, reproducing, diagnosing and fixing software bugs. Many techniques have been proposed to aid developers during software testing and debugging. However, researchers have found that developers hardly use or adopt the proposed techniques in software practice. Evidently, this is because there is a gap between proposed methods and the state of software practice. Most methods fail to address the actual needs of software developers. In this dissertation, we pose the following scientific question: How can we bridge the gap between software practice and the state-of-the-art automated testing and debugging techniques? To address this challenge, we put forward the following thesis: Software testing and debugging should be driven by empirical evidence collected from software practice. In particular, we posit that the feedback from software practice should shape and guide (the automation) of testing and debugging activities. In this thesis, we focus on gathering evidence from software practice by conducting several empirical studies on software testing and debugging activities in the real-world. We then build tools and methods that are well-grounded and driven by the empirical evidence obtained from these experiments. Firstly, we conduct an empirical study on the state of debugging in practice using a survey and a human study. In this study, we ask developers about their debugging needs and observe the tools and strategies employed by developers while testing, diagnosing and repairing real bugs. Secondly, we evaluate the effectiveness of the state-of-the-art automated fault localization (AFL) methods on real bugs and programs. Thirdly, we conducted an experiment to evaluate the causes of invalid inputs in software practice. Lastly, we study how to learn input distributions from real-world sample inputs, using probabilistic grammars. To bridge the gap between software practice and the state of the art in software testing and debugging, we proffer the following empirical results and techniques: (1) We collect evidence on the state of practice in program debugging and indeed, we found that there is a chasm between (available) debugging tools and developer needs. We elicit the actual needs and concerns of developers when testing and diagnosing real faults and provide a benchmark (called DBGBench) to aid the automated evaluation of debugging and repair tools. (2) We provide empirical evidence on the effectiveness of several state-of-the-art AFL techniques (such as statistical debugging formulas and dynamic slicing). Building on the obtained empirical evidence, we provide a hybrid approach that outperforms the state-of-the-art AFL techniques. (3) We evaluate the prevalence and causes of invalid inputs in software practice, and we build on the lessons learned from this experiment to build a general-purpose algorithm (called ddmax) that automatically diagnoses and repairs real-world invalid inputs. (4) We provide a method to learn the distribution of input elements in software practice using probabilistic grammars and we further employ the learned distribution to drive the test generation of inputs that are similar (or dissimilar) to sample inputs found in the wild. In summary, we propose an evidence-driven approach to software testing and debugging, which is based on collecting empirical evidence from software practice to guide and direct software testing and debugging. In our evaluation, we found that our approach is effective in improving the effectiveness of several debugging activities in practice. In particular, using our evidence-driven approach, we elicit the actual debugging needs of developers, improve the effectiveness of several automated fault localization techniques, effectively debug and repair invalid inputs, and generate test inputs that are (dis)similar to real-world inputs. Our proposed methods are built on empirical evidence and they improve over the state-of-the-art techniques in testing and debugging.Software-Debugging bezeichnet das Testen, Aufspüren, Reproduzieren, Diagnostizieren und das Beheben von Fehlern in Programmen. Es wurden bereits viele Debugging-Techniken vorgestellt, die Softwareentwicklern beim Testen und Debuggen unterstützen. Dennoch hat sich in der Forschung gezeigt, dass Entwickler diese Techniken in der Praxis kaum anwenden oder adaptieren. Das könnte daran liegen, dass es einen großen Abstand zwischen den vorgestellten und in der Praxis tatsächlich genutzten Techniken gibt. Die meisten Techniken genügen den Anforderungen der Entwickler nicht. In dieser Dissertation stellen wir die folgende wissenschaftliche Frage: Wie können wir die Kluft zwischen Software-Praxis und den aktuellen wissenschaftlichen Techniken für automatisiertes Testen und Debugging schließen? Um diese Herausforderung anzugehen, stellen wir die folgende These auf: Das Testen und Debuggen von Software sollte von empirischen Daten, die in der Software-Praxis gesammelt wurden, vorangetrieben werden. Genauer gesagt postulieren wir, dass das Feedback aus der Software-Praxis die Automation des Testens und Debuggens formen und bestimmen sollte. In dieser Arbeit fokussieren wir uns auf das Sammeln von Daten aus der Software-Praxis, indem wir einige empirische Studien über das Testen und Debuggen von Software in der echten Welt durchführen. Auf Basis der gesammelten Daten entwickeln wir dann Werkzeuge, die sich auf die Daten der durchgeführten Experimente stützen. Als erstes führen wir eine empirische Studie über den Stand des Debuggens in der Praxis durch, wobei wir eine Umfrage und eine Humanstudie nutzen. In dieser Studie befragen wir Entwickler zu ihren Bedürfnissen, die sie beim Debuggen haben und beobachten die Werkzeuge und Strategien, die sie beim Diagnostizieren, Testen und Aufspüren echter Fehler einsetzen. Als nächstes bewerten wir die Effektivität der aktuellen Automated Fault Localization (AFL)- Methoden zum automatischen Aufspüren von echten Fehlern in echten Programmen. Unser dritter Schritt ist ein Experiment, um die Ursachen von defekten Eingaben in der Software-Praxis zu ermitteln. Zuletzt erforschen wir, wie Häufigkeitsverteilungen von Teileingaben mithilfe einer Grammatik von echten Beispiel-Eingaben aus der Praxis gelernt werden können. Um die Lücke zwischen Software-Praxis und der aktuellen Forschung über Testen und Debuggen von Software zu schließen, bieten wir die folgenden empirischen Ergebnisse und Techniken: (1) Wir sammeln aktuelle Forschungsergebnisse zum Stand des Software-Debuggens und finden in der Tat eine Diskrepanz zwischen (vorhandenen) Debugging-Werkzeugen und dem, was der Entwickler tatsächlich benötigt. Wir sammeln die tatsächlichen Bedürfnisse von Entwicklern beim Testen und Debuggen von Fehlern aus der echten Welt und entwickeln einen Benchmark (DbgBench), um das automatische Evaluieren von Debugging-Werkzeugen zu erleichtern. (2) Wir stellen empirische Daten zur Effektivität einiger aktueller AFL-Techniken vor (z.B. Statistical Debugging-Formeln und Dynamic Slicing). Auf diese Daten aufbauend, stellen wir einen hybriden Algorithmus vor, der die Leistung der aktuellen AFL-Techniken übertrifft. (3) Wir evaluieren die Häufigkeit und Ursachen von ungültigen Eingaben in der Softwarepraxis und stellen einen auf diesen Daten aufbauenden universell einsetzbaren Algorithmus (ddmax) vor, der automatisch defekte Eingaben diagnostiziert und behebt. (4) Wir stellen eine Methode vor, die Verteilung von Schnipseln von Eingaben in der Software-Praxis zu lernen, indem wir Grammatiken mit Wahrscheinlichkeiten nutzen. Die gelernten Verteilungen benutzen wir dann, um den Beispiel-Eingaben ähnliche (oder verschiedene) Eingaben zu erzeugen. Zusammenfassend stellen wir einen auf der Praxis beruhenden Ansatz zum Testen und Debuggen von Software vor, welcher auf empirischen Daten aus der Software-Praxis basiert, um das Testen und Debuggen zu unterstützen. In unserer Evaluierung haben wir festgestellt, dass unser Ansatz effektiv viele Debugging-Disziplinen in der Praxis verbessert. Genauer gesagt finden wir mit unserem Ansatz die genauen Bedürfnisse von Entwicklern, verbessern die Effektivität vieler AFL-Techniken, debuggen und beheben effektiv fehlerhafte Eingaben und generieren Test-Eingaben, die (un)ähnlich zu Eingaben aus der echten Welt sind. Unsere vorgestellten Methoden basieren auf empirischen Daten und verbessern die aktuellen Techniken des Testens und Debuggens