A quantitative study of Java software buildability

Sulír, Matúš; Porubãn, Jaroslav

doi:10.1145/3001878.3001882

Cited by 32 publications

(31 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Recent work has shown that managing dependencies is a large and time consuming problem developers face when engaging in configuration management [18], [8], [19], [3], [9]. In particular, some developers have highlighted dependency management as one of the largest problems facing Python, requiring developers to spend non-trivial amounts of time on configuration for basic tasks.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…Unfortunately, many code snippets require a non-trivial environment configuration in order to execute successfully [7], [8], and are not accompanied by sufficient information for developers to easily recreate that configuration [3], [9], [10]. This leads to the problem of configuration drift, where a code snippet goes out-of-date because the APIs that it depends on experience breaking changes over time.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

V2: Fast Detection of Configuration Drift in Python

Horton

Parnin

2019

2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)

View full text Add to dashboard Cite

Code snippets are prevalent, but are hard to reuse because they often lack an accompanying environment configuration. Most are not actively maintained, allowing for drift between the most recent possible configuration and the code snippet as the snippet becomes out-of-date over time. Recent work has identified the problem of validating and detecting out-of-date code snippets as the most important consideration for code reuse. However, determining if a snippet is correct, but simply out-of-date, is a non-trivial task. In the best case, breaking changes are well documented, allowing developers to manually determine when a code snippet contains an out-of-date API usage. In the worst case, determining if and when a breaking change was made requires an exhaustive search through previous dependency versions.We present V2, a strategy for determining if a code snippet is out-of-date by detecting discrete instances of configuration drift, where the snippet uses an API which has since undergone a breaking change. Each instance of configuration drift is classified by a failure encountered during validation and a configuration patch, consisting of dependency version changes, which fixes the underlying fault. V2 uses feedback-directed search to explore the possible configuration space for a code snippet, reducing the number of potential environment configurations that need to be validated. When run on a corpus of public Python snippets from prior research, V2 identifies 248 instances of configuration drift.

show abstract

Section: Discussion and Future Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

V2: Fast Detection of Configuration Drift in Python

Horton

Parnin

2019

2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)

View full text Add to dashboard Cite

show abstract

“…Although in this paper we are focused on runtime information, the approach can be extended to map practically any machine-produced information to a piece of code -output from static analysis [25], build log messages [26], etc.…”

Section: Discussionmentioning

confidence: 99%

Exposing Runtime Information through Source Code Annotations

SulĂr¹,

Porubãn²

2017

AEI

View full text Add to dashboard Cite

Many tools support exploration and analysis of various run-time properties of programs. However, the results produced by these tools are often discarded or stored in custom external files. We discuss an approach where data obtained when executing the program are written as Java annotations over program elements. First, the viability of this approach is demonstrated by a feature annotator, which assigns features to corresponding methods. Second, we describe other types of run-time information that we consider suitable for inclusion in the source code. We also suggest two possible workflows how generated annotations can be used in the development process.

show abstract

“…We focus the related work discussion in two areas: compilability [9,10,13,15,16,20] and software quality evolution [1,7,[17][18][19]22] by commit-level.…”

Section: Related Workmentioning

confidence: 99%

A scalable and efficient approach for compiling and analyzing commit history

Behnamghader¹,

Meemeng²,

Fostiropoulos³

et al. 2018

Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement

View full text Add to dashboard Cite

Background: Researchers oftentimes measure quality metrics only in the changed files when analyzing software evolution over commithistory. This approach is not suitable for compilation and using program analysis techniques that require byte-code. At the same time, compiling the whole software not only is costly but may also leave us with many uncompilable and unanalyzed revisions. Aims: We intend to demonstrate if analyzing changes in a module results in achieving a high compilation ratio and a better understanding of software quality evolution. Method: We conduct a large-scale multiperspective empirical study on 37838 distinct revisions of the core module of 68 systems across Apache, Google, and Netflix to assess their compilability and identify when the software is uncompilable as a result of a developer's fault. We study the characteristics of uncompilable revisions and analyze compilable ones to understand the impact of developers on software quality. Results: We achieve high compilation ratios: 98.4% for Apache, 99.0% for Google, and 94.3% for Netflix. We identify 303 sequences of uncompile commits and create a model to predict uncompilability based on commit metadata with an F1-score of 0.89 and an AUC of 0.96. We identify statistical differences between the impact of affiliated and external developers of organizations. Conclusions: Focusing on a module results in a more complete and accurate software evolution analysis, reduces the cost and complexity, and facilitates manual inspection. CCS CONCEPTS• Software and its engineering → Software configuration management and version control systems; Maintaining software;

show abstract

A quantitative study of Java software buildability

Cited by 32 publications

References 15 publications

V2: Fast Detection of Configuration Drift in Python

V2: Fast Detection of Configuration Drift in Python

Exposing Runtime Information through Source Code Annotations

A scalable and efficient approach for compiling and analyzing commit history

Contact Info

Product

Resources

About