Tracing the Scenarios in Scenario-Based Product Design: a study to support scenario generation

Scenario-based design originates from the human-computer interaction and\ud software engineering disciplines, and continues to be adapted for product development. Product development differs from software development in the former’s more varied context of use, broader characteristics of users and more tangible solutions. The possible use of scenarios in product design is therefore broader and more challenging. Existing design methods that involve scenarios can be employed in many different stages of the product design process. However, there is no proficient overview that discusses a\ud scenario-based product design process in its full extent. The purposes of creating scenarios and the evolution of scenarios from their original design data are often not obvious, although the results from using scenarios are clearly visible. Therefore, this paper proposes to classify possible scenario uses with their purpose, characteristics and supporting design methods. The classification makes explicit different types of scenarios and their relation to one another. Furthermore, novel scenario uses can be referred or added to the classification to develop it in parallel with the scenario-based design\ud practice. Eventually, a scenario-based product design process could take inspiration for creating scenarios from the classification because it provides detailed characteristics of the scenario

Automatic Repair of Real Bugs: An Experience Report on the Defects4J Dataset

Defects4J is a large, peer-reviewed, structured dataset of real-world Java bugs. Each bug in Defects4J is provided with a test suite and at least one failing test case that triggers the bug. In this paper, we report on an experiment to explore the effectiveness of automatic repair on Defects4J. The result of our experiment shows that 47 bugs of the Defects4J dataset can be automatically repaired by state-of- the-art repair. This sets a baseline for future research on automatic repair for Java. We have manually analyzed 84 different patches to assess their real correctness. In total, 9 real Java bugs can be correctly fixed with test-suite based repair. This analysis shows that test-suite based repair suffers from under-specified bugs, for which trivial and incorrect patches still pass the test suite. With respect to practical applicability, it takes in average 14.8 minutes to find a patch. The experiment was done on a scientific grid, totaling 17.6 days of computation time. All their systems and experimental results are publicly available on Github in order to facilitate future research on automatic repair

Programming Not Only by Example

In recent years, there has been tremendous progress in automated synthesis techniques that are able to automatically generate code based on some intent expressed by the programmer. A major challenge for the adoption of synthesis remains in having the programmer communicate their intent. When the expressed intent is coarse-grained (for example, restriction on the expected type of an expression), the synthesizer often produces a long list of results for the programmer to choose from, shifting the heavy-lifting to the user. An alternative approach, successfully used in end-user synthesis is programming by example (PBE), where the user leverages examples to interactively and iteratively refine the intent. However, using only examples is not expressive enough for programmers, who can observe the generated program and refine the intent by directly relating to parts of the generated program. We present a novel approach to interacting with a synthesizer using a granular interaction model. Our approach employs a rich interaction model where (i) the synthesizer decorates a candidate program with debug information that assists in understanding the program and identifying good or bad parts, and (ii) the user is allowed to provide feedback not only on the expected output of a program, but also on the underlying program itself. That is, when the user identifies a program as (partially) correct or incorrect, they can also explicitly indicate the good or bad parts, to allow the synthesizer to accept or discard parts of the program instead of discarding the program as a whole. We show the value of our approach in a controlled user study. Our study shows that participants have strong preference to using granular feedback instead of examples, and are able to provide granular feedback much faster

What is the method in applying formal methods to PLC applications?

The question we investigate is how to obtain PLC applications with confidence in their proper functioning. Especially, we are interested in the contribution that formal methods can provide for their development. Our maxim is that the place of a particular formal method in the total picture of system development should be made very clear. Developers and customers ought to understand very well what they can rely on or not, and we see our task in trying to make this explicit. Therefore, for us the answer to the question above leads to the following questions: Which parts of the system can be treated formally? What formal methods and tools can be applied? What does their successful application tell (or does not) about the proper functioning of the whole system

Genie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commands

To understand diverse natural language commands, virtual assistants today are trained with numerous labor-intensive, manually annotated sentences. This paper presents a methodology and the Genie toolkit that can handle new compound commands with significantly less manual effort. We advocate formalizing the capability of virtual assistants with a Virtual Assistant Programming Language (VAPL) and using a neural semantic parser to translate natural language into VAPL code. Genie needs only a small realistic set of input sentences for validating the neural model. Developers write templates to synthesize data; Genie uses crowdsourced paraphrases and data augmentation, along with the synthesized data, to train a semantic parser. We also propose design principles that make VAPL languages amenable to natural language translation. We apply these principles to revise ThingTalk, the language used by the Almond virtual assistant. We use Genie to build the first semantic parser that can support compound virtual assistants commands with unquoted free-form parameters. Genie achieves a 62% accuracy on realistic user inputs. We demonstrate Genie's generality by showing a 19% and 31% improvement over the previous state of the art on a music skill, aggregate functions, and access control.Comment: To appear in PLDI 201