Towards a Theory of Software Development Expertise

Software development includes diverse tasks such as implementing new features, analyzing requirements, and fixing bugs. Being an expert in those tasks requires a certain set of skills, knowledge, and experience. Several studies investigated individual aspects of software development expertise, but what is missing is a comprehensive theory. We present a first conceptual theory of software development expertise that is grounded in data from a mixed-methods survey with 335 software developers and in literature on expertise and expert performance. Our theory currently focuses on programming, but already provides valuable insights for researchers, developers, and employers. The theory describes important properties of software development expertise and which factors foster or hinder its formation, including how developers' performance may decline over time. Moreover, our quantitative results show that developers' expertise self-assessments are context-dependent and that experience is not necessarily related to expertise.Comment: 14 pages, 5 figures, 26th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2018), ACM, 201

Biographien und Alltag von Spitzenmusikern

Die erste Untersuchung aus dem Projekt "Peak Performance" ist abgeschlossen, und an anderer Stelle ausführlicher beschrieben (ERICSSON, KRAMPE & TESCH-RÖMER, 1990). Die Autoren skizzieren in diesem Beitrag ihre Untersuchungsmethode und berichten einige Ergebnisse, die wichtige Aspekte ihres theoretischen Modells überprüfbar machen. (DIPF/Orig.

Using Cognitive Walkthrough and Hybrid Prototyping to Gather User Requirements in Early Design Virtual Reality Prototypes

To evaluate Virtual Reality (VR) prototypes usability involves a va-riety of single-perspective or Hybrid methods. The latter has being suggested by literature as offering a more complete sets of requirements highlighting both ‘in-world’ and user interface problems. This paper describes our experiences in using a single-perspective method for gathering user requirements in the REVERIE (Real and Virtual Engagement In Realistic Immersive Environment) project. The study reports results involving nine evaluators who reviewed two hybrid VR prototypes with educational context. It was found that this approach was effective in highlighting a plethora of usability problems covering all as-pects of the two VR prototypes. The performance of our approach was similar to the literature. Although additional validation work is required, we can con-clude that our approach may provide a viable option to evaluate early design VR prototypes when required (e.g., when the expertise needed to use a hybrid method is not available). Future work aims to compare the performance of our approach with two-stage and multiple stage hybrid methods

Rethinking thinking aloud: A comparison of three think-aloud protocols

This paper presents the results of a study that compared three think-aloud methods: concurrent think-aloud, retrospective think-aloud, and a hybrid method. The three methods were compared through an evaluation of a library website, which involved four points of comparison: task performance, participants' experiences, usability problems discovered, and the cost of employing the methods. The results revealed that the concurrent method outperformed both the retrospective and the hybrid methods in facilitating successful usability testing. It detected higher numbers of usability problems than the retrospective method, and produced output comparable to that of the hybrid method. The method received average to positive ratings from its users, and no reactivity was observed. Lastly, this method required much less time on the evaluator's part than did the other two methods, which involved double the testing and analysis time

Time-dynamic effects on the global temperature when harvesting logging residues for bioenergy

The climate mitigation potential of using logging residues (tree tops and branches) for bioenergy has been debated. In this study, a time-dependent life cycle assessment (LCA) was performed using a single-stand perspective. Three forest stands located in different Swedish climate zones were studied in order to assess the global temperature change when using logging residues for producing district heating. These systems were compared with two fossil reference systems in which the logging residues were assumed to remain in the forest to decompose over time, while coal or natural gas was used for energy. The results showed that replacing coal with logging residues gave a direct climate benefit from a single-stand perspective, while replacing natural gas gave a delayed climate benefit of around 8-12 years depending on climate zone. A sensitivity analysis showed that the time was strongly dependent on the assumptions for extraction and combustion of natural gas. The LCA showed that from a single-stand perspective, harvesting logging residues for bioenergy in the south of Sweden would give the highest temperature change mitigation potential per energy unit. However, the differences between the three climate zones studied per energy unit were relatively small. On a hectare basis, the southern forest stand would generate more biomass compared to the central and northern locations, which thereby could replace more fossil fuel and give larger climate benefits

Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns, or design choices in the computational implementation. To aid the adoption of ML in TILs assessment, we provide an in-depth discussion of ML and image analysis including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial- and routine clinical management of patients with TNBC

6. Theoretical Implications from Protocol Analysis on Testing and Measurement

One of the goals of psychology has always been to describe, understand, and measure individual differences. The diversity of human behavior makes it particularly challenging to seek to identify general and stable underlying elements that correspond to systematic individual differences . A major problem in the efforts to identify such elements is that the elements cannot be observed directly. The primary method has been to use the current psychological theory to develop procedures to measure such hypothetical elements. In this chapter I present a new theoretic framework, based on verbal reports from subjects, for identifying and measuring individual differences. I argue that this framework is superior to the previous ones; hence, I briefly review some of the earlier approaches to measurement of individual differences. When scientific psychology was first established over 100 years ago , the predominant method of investigation consisted of eliciting introspective verbal reports from trained observers. During the introspective era, the research was directed toward uncovering the basic sensations and cognitive processes that provided the building blocks of the varied and complex human experiences. Within this theoretical perspective, it was assumed that observable individual differences in normal cognitive functioning were a consequence of differences in basic cognitive processes. It was furthermore assumed that individual differences in performance on simple tasks, like simple reaction time, letter cancellation, and sensory discrimination, would directly reflect individual differences in the corresponding basic processes. However, the first studies of individual differences on simple tasks showed disappointingly low correlations among tasks as well as to grades in school and other indices of ability (Guilford , 1967). Particularly damaging for this view of simple tasks reflecting basic processes was the finding that substantial improvement in performance was observed with practice (Binet cited in Varon , 1935). Although subsequent successful attempts to measure intelligence reliably relied almost exclusively on complex tasks involving comprehension, the view that individual differences are due to differences in basic processes was never completely discarded. Exceptional ability (exceptional memory) was consistently interpreted as a result of differences in such basic processes. The behavioristic era had interesting implications for measurement, in that a theory of cognitive structures was explicitly rejected. Among extreme behaviorists, all individual differences were attributed to differences in learning, or exposure to relevant experiences. Hence, measurement of basic cognitive functions would be meaningless. The measurement of individual differences in complex tasks had to be conducted in an inductive mode, where stable patterns of individual differences were discovered empirically rather than deduced theoretically. Lacking a cognitive theory, a general theory of measurement was developed and refined through the years. This theory of measurement was incorporated as an integral part of the methodology of experimental psychology. A central problem with the behavioristic approach was to understand what the observed performance on a test actually measures