Exploring exploratory programming

Kery, Mary Beth; Myers, Brad A.

doi:10.1109/vlhcc.2017.8103446

Cited by 72 publications

(49 citation statements)

References 23 publications

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“…Woodbury et al [44] define alternatives as "structurally different solutions to a design," while variations are "design solutions with identical model structure, but having different values assigned to parameters." Recent studies on data scientists' work practices have found that they write alternative versions of exploratory code [2,16,24,36] or use multiple alternative models, typically generated by different classes of 1 machine-learning algorithms [18]. Even within a single model, alternatives can also exist.…”

Section: Various Concepts Around Alternativesmentioning

confidence: 99%

Understanding the Role of Alternatives in Data Analysis Practices

Liu

Boukhelifa

Eagan

2019

IEEE Trans. Visual. Comput. Graphics

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Data workers are people who perform data analysis activities as a part of their daily work but do not formally identify as data scientists. They come from various domains and often need to explore diverse sets of hypotheses and theories, a variety of data sources, algorithms, methods, tools, and visual designs. Taken together, we call these alternatives. To better understand and characterize the role of alternatives in their analyses, we conducted semi-structured interviews with 12 data workers with different types of expertise. We conducted four types of analyses to understand 1) why data workers explore alternatives; 2) the different notions of alternatives and how they fit into the sensemaking process; 3) the high-level processes around alternatives; and 4) their strategies to generate, explore, and manage those alternatives. We find that participants' diverse levels of domain and computational expertise, experience with different tools, and collaboration within their broader context play an important role in how they explore these alternatives. These findings call out the need for more attention towards a deeper understanding of alternatives and the need for better tools to facilitate the exploration, interpretation, and management of alternatives. Drawing upon these analyses and findings, we present a framework based on participants' 1) degree of attention, 2) abstraction level, and 3) analytic processes. We show how this framework can help understand how data workers consider such alternatives in their analyses and how tool designers might create tools to better support them.

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Section: Various Concepts Around Alternativesmentioning

confidence: 99%

Understanding the Role of Alternatives in Data Analysis Practices

Liu

Boukhelifa

Eagan

2019

IEEE Trans. Visual. Comput. Graphics

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“…In figure 7a, the user loads a png image (as shown in the output cell [1]). Then a blur function is defined in the input cell [2], which does not produce an output but is now available for use. Then, in figure 7b, the blur function is invoked on the input image in.…”

Section: Halide*mentioning

confidence: 99%

“…Mainly, notebooks offer a different GUI for interacting with code and documentation. This interface does not clash with the more traditional GUIs such as IDEs or text editors, but it offers an enhanced interface mainly for end-user programming [35] and exploratory programming [2,29,56].…”

Section: Related Workmentioning

confidence: 99%

“…Currently, there are several studies tackling usability, cognitive, and reproducibility aspects of computational notebooks. For instance, in reproducibility [36,47,48,58], education [44], exploratory programming [2,18,29,30], documentation [76], and data journalism [78]. However, no attention has been paid from the language engineering point of view.…”

Section: :21mentioning

confidence: 99%

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Bacatá: Notebooks for DSLs, Almost for Free

2020

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Context: Computational notebooks are a contemporary style of literate programming, in which users can communicate and transfer knowledge by interleaving executable code, output, and prose in a single rich document. A Domain-Specific Language (DSL) is a software language tailored for an application domain. Usually, DSL users are domain experts that may not have a software engineering background. Therefore, they might not be familiar with Integrated Development Environments (IDEs). In brief, the development of tools that offer different interfaces for interacting with a DSL is relevant.Inquiry: However, DSL designers' resources are limited. We want to leverage General-purpose Languages (GPLs) tooling in the context of DSLs. Computational notebooks are an example of such tools. Then, our main question is: What is an efficient and effective method of designing and implementing notebook interfaces for DSLs? By addressing this question, we might be able to speed up the development of DSL tools, and ease the interaction between end-users and DSLs.Approach: In this paper, we present Bacatá, a mechanism for generating notebook interfaces for external DSLs in a language-parametric fashion. This mechanism is designed in a way in which language engineers can reuse as many language components as possible (e.g., language processors, type checkers, code generators). In addition, we present a Feature-Oriented Domain Analysis that depicts language dependent and language independent features of computational notebooks.Knowledge: Our results show that notebook interfaces generated by Bacatá can be used with little manual configuration. However, there are a few considerations and caveats that should be addressed by language engineers that rely on language design aspects. The creation of a notebook for a DSL with Bacatá becomes a matter of writing the code that wires existing language components in the Rascal language workbench with the Jupyter platform.Grounding: We evaluate Bacatá by generating functional computational notebook interfaces for three different non-trivial DSLs, namely: a small subset of Halide (a DSL for digital image processing), SweeterJS (an extended version of JavaScript), and QL (a DSL for questionnaires). Additionally, it is relevant to generate notebook implementations rather than implementing them manually. To illustrate this, we measured and compared the number of source lines of code that we reused from existing implementations of those languages.Importance: The adoption of notebooks by novice-programmers and end-users has made them very popular in several domains such as exploratory programming, data science, data journalism, and machine learning. Why are they popular? In (data) science, it is essential to make results reproducible as well as understandable. However, notebooks are only available for GPLs. This paper opens the notebook metaphor for DSLs to improve the end-user experience when interacting with code and to increase DSLs adoption. ACM CCS 2012Software and its engineering → Domain specific lang...

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“…The notions of exploratory and live programming have been extensively studied [50]. The notion of exploratory programming has recently been analysed from the perspective of human-computer interaction [28], which led to new tools [27], complementary to our instant previews. Kubelka, Robbes, and Bergel [31] review the use of live programming in a Smalltalk derived environment.…”

Section: Related and Future Workmentioning

confidence: 99%

Foundations of a live data exploration environment

Petříček

2020

Programming

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A growing amount of code is written to explore and analyze data, often by data analysts who do not have a traditional background in programming, for example by journalists. The way such data anlysts write code is different from the way software engineers do so. They use few abstractions, work interactively and rely heavily on external libraries. We aim to capture this way of working and build a programming environment that makes data exploration easier by providing instant live feedback.We combine theoretical and applied approach. We present the data exploration calculus, a formal language that captures the structure of code written by data analysts. We then implement a data exploration environment that evaluates code instantly during editing and shows previews of the results. We formally describe an algorithm for providing instant previews for the data exploration calculus that allows the user to modify code in an unrestricted way in a text editor. Supporting interactive editing is tricky as any edit can change the structure of code and fully recomputing the output would be too expensive. We prove that our algorithm is correct and that it reuses previous results when updating previews after a number of common code edit operations. We also illustrate the practicality of our approach with an empirical evaluation and a case study.As data analysis becomes an ever more important use of programming, research on programming languages and tools needs to consider new kinds of programming workflows appropriate for those domains and conceive new kinds of tools that can support them. The present paper is one step in this important direction. ACM CCS 2012Human-centered computing → Interactive systems and tools; Information systems → Data mining;Software and its engineering → Compilers;Foundations of a live data exploration environment development and execution. Data analysts write small snippets of code, run them to see results immediately and then revise them.Notebooks are used by users ranging from scientists who implement complex models of physical systems to journalists who perform simple data aggregations and create visualizations. Our focus is on the simplest use cases. Making programmatic data exploration more spreadsheet-like should encourage users to choose programming tools over spreadsheets, resulting in more reproducible and transparent data analyses.Consider the Financial Times analysis of plastic waste [7,25]. It joins datasets from Eurostat, UN Comtrade and more, aggregates the data and builds a visualization comparing waste flows in 2017 and 2018. Figure 1 shows an excerpt from one notebook of the data analysis. The code has a number of important properties:There is no abstraction. The analysis uses lambda functions as arguments to library calls, but it does not define any custom functions. Code is parameterized by having a global variable material set to "plastics" and keeping other possible values in a comment. This lets the analyst run and check results of intermediate steps.The code relies on external lib...

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Exploring exploratory programming

Cited by 72 publications

References 23 publications

Understanding the Role of Alternatives in Data Analysis Practices

Understanding the Role of Alternatives in Data Analysis Practices

Bacatá: Notebooks for DSLs, Almost for Free

Foundations of a live data exploration environment

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