Fraunhofer USA Center Mid-Atlantic CMA

Software organizations’ main assets are not plants, buildings, or
\nexpensive machines. A software organization’s main asset is its intellectual
\ncapital, as it is in sectors such as consulting, law, investment
\nbanking, and advertising. The major problem with intellectual
\ncapital is that it has legs and walks home every day. At the same rate
\nexperience walks out the door, inexperience walks in the door. Whether or
\nnot many software organizations admit it, they face the challenge of sustaining the level of competence needed to
\nwin contracts and fulfill undertakings.

Graphical user interfaces (GUIs) are important parts of today’s software and their correct execution is required to ensure the correctness of the overall software. A popular technique to detect defects in GUIs is to test them by executing test cases and checking the execution results. Test cases may either be created manually or generated automatically from a model of the GUI. While manual testing is unacceptably slow for many applications, our experience with GUI testing has shown that creating a model that can be used for automated test case generation is difficult. We describe a new approach to reverse engineer a model represented as structures called a GUI forest, event-flow graphs and an integration tree directly from the executable GUI. We describe “GUI Ripping”, a dynamic process in which the software’s GUI is automatically “traversed ” by opening all its windows and extracting all their widgets (GUI objects), properties, and values. The extracted information is then verified by the test designer and used to automatically generate test cases. We present algorithms for the ripping process and describe their implementation in a tool suite that operates on Java and Microsoft Windows ’ GUIs. We present results of case studies which show that our approach requires very little human intervention and is especially useful for regression testing of software that is modified frequently. We have successfully used the “GUI Ripper” in several large experiments and have made it available as a downloadable tool. 1

Several empirical studies have been carried out with college students as subjects in the last few years. Researchers often use these studies to pilot experiments before they are carried out in industrial environments. Reports on these studies usually focus on the results obtained and issues such as their external validity. However, the effects and value of empirical studies with students may go beyond the contribution to scientific literature. For instance, the pedagogical challenges and value of these studies is hardly ever stressed. We identify four primary actors that are involved in these empirical studies, i.e., researchers, students, instructors, and industry. We discuss the costs and benefits of empirical studies with students for these actors, which are different because of the actors' different goals, expectations, and constraints, which must be recognized to fully exploit empirical studies with students. We also provide some advice on how to carry out empirical studies with students based on our experiences.

Existing research is unclear on how to generate lessons learned for defect prediction and effort estimation. Should we seek lessons that are global to multiple projects or just local to particular projects? This paper aims to comparatively evaluate local versus global lessons learned for effort estimation and defect prediction. We applied automated clustering tools to effort and defect datasets from the PROMISE repository. Rule learners generated lessons learned from all the data, from local projects, or just from each cluster. The results indicate that the lessons learned after combining small parts of different data sources (i.e., the clusters) were superior to either generalizations formed over all the data or local lessons formed from particular projects. We conclude that when researchers attempt to draw lessons from some historical data source, they should 1) ignore any existing local divisions into multiple sources, 2) cluster across all available data, then 3) restrict the learning of lessons to the clusters from other sources that are nearest to the test data.

Background: Existing software defect classification schemes support multiple tasks, such as root cause analysis and process improvement guidance. However, existing schemes do not assist in assigning defects to a broad range of high level software goals, such as software quality characteristics like functionality, maintainability, and usability. Aim: We investigate whether a classification based on the ISO/IEC 9126 software product quality model is reliable and useful to link defects to quality aspects impacted. Method: Six different subjects, divided in two groups with respect to their expertise, classified 78 defects from an industrial web application using the ISO/IEC 9126 quality main characteristics and sub-characteristics, and a set of proposed extended guidelines. Results: The ISO/IEC 9126 model is reasonably reliable when used to classify defects, even using incomplete defect reports. Reliability and variability is better for the six high level main characteristics of the model than for the 22 subcharacteristics. Conclusions: The ISO/IEC 9126 software quality model provides a solid foundation for defect classification. We also recommend, based on the follow up qualitative analysis performed, to use more complete defect reports and tailor the quality model to the context of use.

OBJECTIVES: This longitudinal study examined the prevalence and correlates of energy drink use among college students, and investigated its possible prospective associations with subsequent drug use, including nonmedical prescription drug use. METHODS: Participants were 1,060 undergraduates from a large, public university who completed three annual interviews, beginning in their first year of college. Use of energy drinks, other caffeinated products, tobacco, alcohol, and other illicit and prescription drugs were assessed, as well as demographic and personality characteristics. RESULTS: Annual weighted prevalence of energy drink use was 22.6%(wt) and 36.5%(wt) in the second and third year of college, respectively. Compared to energy drink non-users, energy drink users had heavier alcohol consumption patterns, and were more likely to have used other drugs, both concurrently and in the preceding assessment. Regression analyses revealed that Year 2 energy drink use was significantly associated with Year 3 nonmedical use of prescription stimulants and prescription analgesics, but not with other Year 3 drug use, holding constant demographics, prior drug use, and other factors. CONCLUSIONS: A substantial and rapidly-growing proportion of college students use energy drinks. Energy drink users tend to have greater involvement in alcohol and other drug use and higher levels of sensation-seeking, relative to non-users of energy drinks. Prospectively, energy drink use has a unique relationship with nonmedical use of prescription stimulants and analgesics. More research is needed regarding the health risks associated with energy drink use in young adults, including their possible role in the development of substance use problems.

Technical debt is a metaphor describing situations where developers accept sacrifices in one dimension of development (e.g. software quality) in order to optimize another dimension (e.g. implementing necessary features before a deadline). Approaches, such as code smell detection, have been developed to identify particular kinds of debt, e.g. design debt. What has not yet been understood is the impact design debt has on the quality of a software product. Answering this question is important for understanding how growing debt affects a software product and how it slows down development, e.g. though introducing rework such as fixing bugs. In this case study we investigate how design debt, in the form of god classes, affects the maintainability and correctness of software products by studying two sample applications of a small-size software development company. The results show that god classes are changed more often and contain more defects than non-god classes. This result complements findings of earlier research and suggests that technical debt has a negative impact on software quality, and should therefore be identified and managed closely in the development process.

There is a growing interest in empirical study in software engineering, both for validating mature technologies and for guiding improvements of less-mature technologies. This paper introduces an empirical methodology, based on experiences garnered over more than two decades of work by the Empirical Software Engineering Group at the University of Maryland and related organizations, for taking a newly proposed improvement to development processes from the conceptual phase through transfer to industry. The methodology presents a series of questions that should be addressed, as well as the types of studies that best address those questions. The methodology is illustrated by a specific research program on inspection processes for Object-Oriented designs. Specific examples of the studies that were performed and how the methodology impacted the development of the inspection process are also described.

The management of technical debt ultimately requires decision making - about incurring, paying off, or deferring technical debt instances. This position paper discusses several existing approaches to complex decision making, and suggests that exploring their applicability to technical debt decision making would be a worthwhile subject for further research.

Although graphical user interfaces (GUIs) constitute a large part of the software being developed today and are typically created using rapid prototyping, there are no effective regression testing techniques for GUIs. The needs of GUI regression testing differ from those of traditional software. When the structure of a GUI is modified, test cases from the original GUI are either reusable or unusable on the modified GUI. Since GUI test case generation is expensive, our goal is to make the unusable test cases usable. The idea of reusing these unusable (a.k.a. obsolete) test cases has not been explored before. In this paper, we show that for GUIs, the unusability of a large number of test cases is a serious problem. We present a novel GUI regression testing technique that first automatically determines the usable and unusable test cases from a test suite after a GUI modification. It then determines which of the unusable test cases can be repaired so they can execute on the modified GUI. The last step is to repair the test cases. Our technique is integrated into a GUI testing framework that, given a test case, automatically executes it on the GUI. We implemented our regression testing technique and demonstrate for two case studies that our approach is effective in that many of the test cases can be repaired, and is practical in terms of its time performance.

Computational scientists developing software for HPC systems face unique software engineering issues. Attempts to transfer SE technologies to this domain must take these issues into account.

Pair programming has many claimed benefits over individual programming. Researchers examined all 15 existing studies of pair programming to determine whether working in pairs is more effective than working individually.

Context: The technical debt (TD) concept describes a tradeoff between short-term and long-term goals in software development. While it is highly useful as a metaphor, it has utility beyond the facilitation of discussion, to inspire a useful set of methods and tools that support the identification, measurement, monitoring, management, and payment of TD. Objective: This study focuses on the identification of TD. We evaluate human elicitation of TD and compare it to automated identification. Method: We asked a development team to identify TD items in artifacts from a software project on which they were working. We provided the participants with a TD template and a short questionnaire. In addition, we also collected the output of three tools to automatically identify TD and compared it to the results of human elicitation. Results: There is little overlap between the TD reported by different developers, so aggregation, rather than consensus, is an appropriate way to combine TD reported by multiple developers. The tools used are especially useful for identifying defect debt but cannot help in identifying many other types of debt, so involving humans in the identification process is necessary. Conclusion: We have conducted a case study that focuses on the practical identification of TD, one area that could be facilitated by tools and techniques. It contributes to the TD landscape, which depicts an understanding of relationships between different types of debt and how they are best discovered.

For 25 years the NASA/GSFC Software Engineering Laboratory (SEL) has been a major resource in software process improvement activities. But due to a changing climate at NASA, agency reorganization, and budget cuts, the SEL has lost much of its impact. In this paper we describe the history of the SEL and give some lessons learned on what we did right, what we did wrong, and what others can learn from our experiences. We briefly describe the research that was conducted by the SEL, describe how we evolved our understanding of software process improvement, and provide a set of lessons learned and hypotheses that should enable future groups to learn from and improve on our quarter century of experiences.

Summary We analyse item level keystroke data from cycle 6 of the National Survey of Family Growth, which is a survey on fertility and related topics that is conducted in the USA. The National Survey of Family Growth is conducted among both males and females by using computer-assisted personal visit interviews and an audio computer-assisted self-interviewing component for the most sensitive topics. Our analyses focus on the time taken to answer a question as a function of item level characteristics, respondent characteristics and interviewer characteristics. Using multilevel models, we explore how these factors influence response times. Our exploratory study suggests that factors at all three levels (item, respondent and interviewer) influence response times. These results demonstrate that question features that explain variation in response times can be automatically derived from standard computer-assisted personal interviewing paradata. The effects of respondent characteristics that we observe are in line with prior findings from more controlled studies conducted in supervised telephone facilities. Some demographic characteristics of interviewers contributed to the variation in response times, though they failed to explain large portions of the between-interviewer variance.

Test designers widely believe that the overall effectiveness and cost of software testing depends largely on the type and number of test cases executed on the software. In this paper we show that the test oracle used during testing also contributes significantly to test effectiveness and cost. A test oracle is a mechanism that determines whether software executed correctly for a test case. We define a test oracle to contain two essential parts: oracle information that represents expected output; and an oracle procedure that compares the oracle information with the actual output. By varying the level of detail of oracle information and changing the oracle procedure, a test designer can create different types of test oracles. We design 11 types of test oracles and empirically compare them on four software systems. We seed faults in software to create 100 faulty versions, execute 600 test cases on each version, for all 11 types of oracles. In all, we report results of 660,000 test runs on software. We show (1) the time and space requirements of the oracles, (2) that faults are detected early in the testing process when using detailed oracle information and complex oracle procedures, although at a higher cost per test case, and (3) that employing expensive oracles results in detecting a large number of faults using relatively smaller number of test cases.

The work described in this paper is an investigation of COTS-based software development within a particular NASA environment, with an emphasis on the processes used. Fifteen projects using a COTS-based approach were studied and their actual process was documented. This process is evaluated to identify essential differences in comparison to traditional software development. The main differences, and the activities for which projects require more guidance, are requirements definition and COTS selection, high level design, integration and testing.

There is a growing interest in empirical study in software engineering, both for validating mature technologies and for guiding improvements of less-mature technologies. This paper introduces an empirical methodology, based on experiences garnered over more than two decades of work by the Empirical Software Engineering Group at the University of Maryland and related organizations, for taking a newly proposed improvement to development processes from the conceptual phase through transfer to industry. The methodology presents a series of questions that should be addressed, as well as the types of studies that best address those questions. The methodology is illustrated by a specific research program on inspection processes for Object-Oriented designs. Specific examples of the studies that were performed and how the methodology impacted the development of the inspection process are also described.

One challenge in software engineering education is to give students sufficient hands-on experience in actually building software. This is necessary so that students can understand which practices and techniques are useful in various situations. Some researchers have advocated alternative teaching methods to help in this regard. If successful, such methods could give students some experience with different approaches' effects in a shorter, more constrained time period. We examine one such approach, game-based learning, here.

Human capital is the main asset of many companies, whose knowledge has to be preserved and leveraged from individual to the company level, allowing continual learning and improvement. Knowledge management has various components and aspects such as socio‐cultural, organizational, and technological. In this paper we address the technological aspect; more precisely we survey available software systems that support different knowledge management activities. We categorize these tools into classes, based on their capabilities and functionality and show what tasks and knowledge processing operations they support.