RECORD
Using Software Categories for the Development of Generative Software
Software Engineering · Submitted 8 Sep 2015
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim This approach helps the generator developer in finding candidates for generated code more easily and systematically than searching by hand and is a step towards tool-supported development of generative software.
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
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The abstract, sentence by sentence
In model-driven development (MDD) software emerges by systematically transforming abstract models to concrete source code. Ideally, performing those transformations is to a large extent the task of code generators. One approach for developing a new code generator is to write a reference implementation and separate it into handwritten and generatable code. Typically, the generator developer manually performs this separation a process that is often time-consuming, labor-intensive, difficult to maintain and may produce more code than necessary. Software categories provide a way for separating code into designated parts with defined dependencies, for example, "Business Logic" code that may not directly use "Technical" code. This paper presents an approach that uses the concept of software categories to semi-automatically determine candidates for generated code. The main idea is to iteratively derive the categories for uncategorized code from the dependencies of categorized code. The candidates for generated or handwritten code finally are code parts belonging to specific (previously defined) categories. This approach helps the generator developer in finding candidates for generated code more easily and systematically than searching by hand and is a step towards tool-supported development of generative software.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | In model-driven development (MDD) software emerges by systematically transforming abstract models to concrete source code. | background 1.00 | 0.05 | 0.03 | asserted 1.00 |
| s1 | Ideally, performing those transformations is to a large extent the task of code generators. | background 1.00 | 0.04 | 0.02 | asserted 0.49 |
| s2 | One approach for developing a new code generator is to write a reference implementation and separate it into handwritten and generatable code. | background 0.91 | 0.09 | 0.03 | asserted 0.76 |
| s3 | Typically, the generator developer manually performs this separation a process that is often time-consuming, labor-intensive, difficult to maintain and may produce more code than necessary. | background 0.92 | 0.09 | 0.02 | asserted 0.80 |
| s4 | Software categories provide a way for separating code into designated parts with defined dependencies, for example, "Business Logic" code that may not directly use "Technical" code. | background 0.97 | 0.06 | 0.03 | asserted 0.97 |
| s5 | This paper presents an approach that uses the concept of software categories to semi-automatically determine candidates for generated code. | contribution 0.99 | 0.80 | 0.25 | asserted 1.00 |
| s6 | The main idea is to iteratively derive the categories for uncategorized code from the dependencies of categorized code. | method 0.98 | 0.59 | 0.09 | asserted 1.00 |
| s7 | The candidates for generated or handwritten code finally are code parts belonging to specific (previously defined) categories. | method 0.91 | 0.48 | 0.16 | asserted 1.00 |
| s8 | This approach helps the generator developer in finding candidates for generated code more easily and systematically than searching by hand and is a step towards tool-supported development of generative software. | interpretation 0.89 | 0.60 | 0.74 | asserted 0.92 |
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The paper
Original abstract
In model-driven development (MDD) software emerges by systematically transforming abstract models to concrete source code. Ideally, performing those transformations is to a large extent the task of code generators. One approach for developing a new code generator is to write a reference implementation and separate it into handwritten and generatable code. Typically, the generator developer manually performs this separation a process that is often time-consuming, labor-intensive, difficult to maintain and may produce more code than necessary. Software categories provide a way for separating code into designated parts with defined dependencies, for example, "Business Logic" code that may not directly use "Technical" code. This paper presents an approach that uses the concept of software categories to semi-automatically determine candidates for generated code. The main idea is to iteratively derive the categories for uncategorized code from the dependencies of categorized code. The candidates for generated or handwritten code finally are code parts belonging to specific (previously defined) categories. This approach helps the generator developer in finding candidates for generated code more easily and systematically than searching by hand and is a step towards tool-supported development of generative software.
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