Measuring multi-language software evolution

Arbuckle, Tom

doi:10.1145/2024445.2024461

Cited by 11 publications

(10 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their tools often grow naturally out of needs within a software ecosystem and are tailored to the peculiarities of a specific language [11], [12], [15], [51]. With regard to multi-language analyses, practitioners also show growing interest in [61], given that an increasing amount of software is written in multiple languages (like web applications and multi-platform mobile apps) [9], [52], [54].…”

Section: Introductionmentioning

confidence: 99%

Reducing redundancies in multi-revision code analysis

Alexandru

Panichella

Gall

2017

2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER)

View full text Add to dashboard Cite

Software engineering research often requires analyzing multiple revisions of several software projects, be it to make and test predictions or to observe and identify patterns in how software evolves. However, code analysis tools are almost exclusively designed for the analysis of one specific version of the code, and the time and resources requirements grow linearly with each additional revision to be analyzed. Thus, code studies often observe a relatively small number of revisions and projects. Furthermore, each programming ecosystem provides dedicated tools, hence researchers typically only analyze code of one language, even when researching topics that should generalize to other ecosystems. To alleviate these issues, frameworks and models have been developed to combine analysis tools or automate the analysis of multiple revisions, but little research has gone into actually removing redundancies in multi-revision, multi-language code analysis. We present a novel end-to-end approach that systematically avoids redundancies every step of the way: when reading sources from version control, during parsing, in the internal code representation, and during the actual analysis. We evaluate our open-source implementation, LISA, on the full history of 300 projects, written in 3 different programming languages, computing basic code metrics for over 1.1 million program revisions. When analyzing many revisions, LISA requires less than a second on average to compute basic code metrics for all files in a single revision, even for projects consisting of millions of lines of code.

show abstract

Section: Introductionmentioning

confidence: 99%

Reducing redundancies in multi-revision code analysis

Alexandru

Panichella

Gall

2017

2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER)

View full text Add to dashboard Cite

show abstract

“…Another interesting aspect of MLSD is the evolution of projects over time; this topic has been investigated by Arbuckle (2011). In this case study of git, it was found that code in scripting languages was not, as expected, replaced by general-purpose language code over time, but that instead the development of all languages proceeded in sync.…”

Section: Multi-language Software Developmentmentioning

confidence: 73%

On multi-language software development, cross-language links and accompanying tools: a survey of professional software developers

Mayer

Kirsch

2017

J Softw Eng Res Dev

View full text Add to dashboard Cite

Context: Non-trivial software systems are written using multiple (programming) languages, which are connected by cross-language links. The existence of such links may lead to various problems during software development. There is little empirical evidence on the incidence of these problems and the experiences of professional developers in this field. Aim: We want to provide empirical evidence on multi-language software development, cross-language linking, and tool support in industry, including the views of professional developers on benefits and problems in these areas. Methods: We conducted a survey study to gather responses from 139 professional software developers. Results: Respondents reported an average of 7 languages and 3 linked language pairs per project. Respondents saw benefits of multi-language development for the motivation of developers and the translation of requirements, but problems in understandability and changeability. Over 90% of respondents reported problems related to cross-language linking. Developers universally agree on the usefulness of tool support. Conclusions: Multi-language programming and cross-language linking seem common but lead to several problems. We suggest that future practical as well as research efforts focus on these issues by creating appropriate tool support and by developing better techniques for cross-language linking for improved changeability and understandability.

show abstract

“…As a final demonstration of LISA's adaptability, we go through the process of adding support for another programming language, namely Lua, to LISA. 11 As discussed in Section 2.3, programming languages share some similarities, especially regarding structure (like how different AST node types are nested), even though they use different grammars and absolutely differ in certain ways. For example, Lua does not have classes as a language-level concept.…”

Section: Adapting Lisa To Other Programming Languagesmentioning

confidence: 99%

“…Given the multi-language nature of today's software projects, researchers have been working on approaches for multi-language program analysis [11,26,50]. These approaches are typically built on metamodels [26,78] that enable the analysis and manipulation of multi-language software code, thus allowing the generalization of specific static analyses and other software recommendations.…”

Section: Multi-language Analysismentioning

confidence: 99%

“…This is the case even though many concepts (like methods, attributes and cyclomatic complexity) are very similar in different programming languages. While multi-language program analysis has been explored in recent literature [11,50], working approaches are usually based on a concrete metamodel [26,78,82] for the analysis or manipulation of multi-language software code. However, these models are comparatively heavy-weight and always require a translation from an existing program representation (such as an AST) to an instance of the model.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Redundancy-free analysis of multi-revision software artifacts

Alexandru¹,

Panichella²,

Proksch³

et al. 2018

Empir Software Eng

View full text Add to dashboard Cite

Researchers often analyze several revisions of a software project to obtain historical data about its evolution. For example, they statically analyze the source code and monitor the evolution of certain metrics over multiple revisions. The time and resource requirements for running these analyses often make it necessary to limit the number of analyzed revisions, e.g., by only selecting major revisions or by using a coarse-grained sampling strategy, which could remove significant details of the evolution. Most existing analysis techniques are not designed for the analysis of multi-revision artifacts and they treat each revision individually. However, the actual difference between two subsequent revisions is typically very small. Thus, tools tailored for the analysis of multiple revisions should only analyze these differences, thereby preventing re-computation and storage of redundant data, improving scalability and enabling the study of a larger number of revisions. In this work, we propose the Lean Language-Independent Software Analyzer (LISA), a generic framework for representing and analyzing multi-revisioned software artifacts. It employs a redundancy-free, multi-revision representation for artifacts and avoids re-computation by only analyzing changed artifact fragments across thousands of revisions. The evaluation of our approach consists of measuring the effect of each individual technique incorporated, an in-depth study of LISA resource requirements and a large-scale analysis over 7 million program revisions of 4,000 software projects written in four languages. We show that the time and space requirements for multi-revision analyses can be reduced by multiple orders of magnitude, when compared to traditional, sequential approaches.

show abstract

Measuring multi-language software evolution

Cited by 11 publications

References 24 publications

Reducing redundancies in multi-revision code analysis

Reducing redundancies in multi-revision code analysis

On multi-language software development, cross-language links and accompanying tools: a survey of professional software developers

Redundancy-free analysis of multi-revision software artifacts

Contact Info

Product

Resources

About