Performance evolution blueprint: Understanding the impact of software evolution on performance

Alcocer, Juan Pablo Sandoval; Bergel, Alexandre; Ducasse, Sté́phane; Denker, Marcus

doi:10.1109/vissoft.2013.6650523

Cited by 19 publications

(5 citation statements)

References 19 publications

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“…Visualizing a software system can help to analyze the evolution of software architecture, identify the developer network, find stable software releases, and monitor software quality trends [Diehl (2007a)]. A rich body of studies [Alexandru et al (2019); Burch et al (2011); Kim et al (2020);Sandoval Alcocer et al (2013); Tomida et al (2019)] explored different ways to visualize evolving software systems for making it easily understandable to keep the consistent evolution. Chevalier et al (2007) did a visualization of evolution patterns in C++ source code by rendering syntax matched code blocks in consecutive versions to detect the code fragments which have been changed during evolution.…”

Section: Related Workmentioning

confidence: 99%

Leveraging Structural Properties of Source Code Graphs for Just-In-Time Bug Prediction

Nadim¹,

Mondal²,

Roy³

2022

Preprint

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The most common use of data visualization is to minimize the complexity for proper understanding. A graph is one of the most commonly used representations for understanding relational data. It produces a simplified representation of data that is challenging to comprehend if kept in a textual format. In this study, we propose a methodology to utilize the relational properties of source code in the form of a graph to identify Just-in-Time (JIT) bug prediction in software systems during different revisions of software evolution and maintenance. We presented a method to convert the source codes of commit patches to equivalent graph representations and named it Source Code Graph (SCG). To understand and compare multiple source code graphs, we extracted several structural properties of these graphs, such as the density, number of cycles, nodes, edges, etc. We then utilized the attribute values of those SCGs to visualize and detect buggy software commits. We process more than 246K software commits from 12 subject systems in this investigation. Our investigation on these 12 open-source software projects written in C++ and Java programming languages shows that if we combine the features from SCG with conventional features used in similar studies, we will get the increased performance of Machine Learning (ML) based buggy commit detection models. We also find the increase of F1 Scores in predicting buggy and non-buggy commits statistically significant using the Wilcoxon Signed Rank Test. Since SCG-based feature values represent the style or structural properties of source code updates or changes in the software system, it suggests the importance of careful maintenance of source code style or structure for keeping a software system bug-free.

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Section: Related Workmentioning

confidence: 99%

Leveraging Structural Properties of Source Code Graphs for Just-In-Time Bug Prediction

Nadim¹,

Mondal²,

Roy³

2022

Preprint

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“…Numerous research papers study the effects of variability layers on software's performances or on configuration options: hardware [10,35,43], workloads [19,20,24,30,43], variants [22,37], versions [13,29,37,41], compilation options [15,27] and input data [3,8]. Such studies provide evidence that some layers have a noticeable impact on the software (configuration) layer.…”

Section: Impacts Of Variability Layersmentioning

confidence: 99%

Deep Software Variability: Towards Handling Cross-Layer Configuration

Lesoil

Acher

Blouin

et al. 2021

Proceedings of the 15th International Working Conference on Variability Modelling of Software-Intensive Systems

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Configuring software is a powerful means to reach functional and performance goals of a system. However, many layers (hardware, operating system, input data, etc.), themselves subject to variability, can alter performances of software configurations. For instance, configurations' options of the x264 video encoder may have very different effects on x264's encoding time when used with different input videos, depending on the hardware on which it is executed. In this vision paper, we coin the term deep software variability to refer to the interaction of all external layers modifying the behavior or non-functional properties of a software. Deep software variability challenges practitioners and researchers: the combinatorial explosion of possible executing environments complicates the understanding, the configuration, the maintenance, the debug, and the test of configurable systems. There are also opportunities: harnessing all variability layers (and not only the software layer) can lead to more efficient systems and configuration knowledge that truly generalizes to any usage and context.

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“…Levin et al, proposed a technique to improve the precision of sampling techniques. Sandoval [39,40] worked on identifying speed regression between consecutive versions. Bertuli et al, used metrics to quantify the mass of (often repetitive) generated information [6,17,37].…”

Section: Related Workmentioning

confidence: 99%

Profiling code cache behaviour via events

Tesone

Polito

Ducasse

2021

Proceedings of the 18th ACM SIGPLAN International Conference on Managed Programming Languages and Runtimes

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Virtual machine performance tuning for a given application is an arduous and challenging task. For example, parametrizing the behaviour of the JIT compiler machine code caches affects the overall performance of applications while being rather obscure for final users not knowledgeable about VM internals. Moreover, VM components are often heavily coupled and changes in some parameters may affect several seemingly unrelated components and may have unclear performance impacts. Therefore, choosing the best parametrization requires to have precise information.In this paper, we present Vicoca, a tool that allows VM users and developers to obtain detailed information about the behaviour of the code caches and their interactions with other virtual machine components. We present a complex optimization problem due to the heavy interaction of components in the Pharo VM, and we explain it using Vicoca. The information produced by the tool allows developers to produce an optimized configuration for the VM. Vicoca is based on event recording that are manipulated during offline analysis. Vicoca not only allows us to understand this given problem, but it opens the door to future work such as automatic detection of application characteristics, identification of performance issues, and automatic hinting.CCS Concepts: • Software and its engineering → Runtime environments.

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Performance evolution blueprint: Understanding the impact of software evolution on performance

Cited by 19 publications

References 19 publications

Leveraging Structural Properties of Source Code Graphs for Just-In-Time Bug Prediction

Leveraging Structural Properties of Source Code Graphs for Just-In-Time Bug Prediction

Deep Software Variability: Towards Handling Cross-Layer Configuration

Profiling code cache behaviour via events

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