Many software projects are configurable through compile-time options (e.g., using ./configure) and also through run-time options (e.g., command-line parameters, fed to the software at execution time). Several works have shown how to predict the effect of run-time options on performance. However it is yet to be studied how these prediction models behave when the software is built with different compile-time options. For instance, is the best run-time configuration always the best w.r.t. the chosen compilation options? In this paper, we investigate the effect of compile-time options on the performance distributions of 4 software systems. There are cases where the compiler layer effect is linear which is an opportunity to generalize performance models or to tune and measure runtime performance at lower cost. We also prove there can exist an interplay by exhibiting a case where compile-time options significantly alter the performance distributions of a configurable system. CCS CONCEPTS• Software and its engineering → Software product lines; Software product lines; Software performance; • Computing methodologies → Machine learning.
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.
With large scale and complex configurable systems, it is hard for users to choose the right combination of options (i.e., configurations) in order to obtain the wanted trade-off between functionality and performance goals such as speed or size. Machine learning can help in relating these goals to the configurable system options, and thus, predict the effect of options on the outcome, typically after a costly training step. However, many configurable systems evolve at such a rapid pace that it is impractical to retrain a new model from scratch for each new version. In this paper, we propose a new method to enable transfer learning of binary size predictions among versions of the same configurable system. Taking the extreme case of the Linux kernel with its ≈ 14, 500 configuration options, we first investigate how binary size predictions of kernel size degrade over successive versions. We show that the direct reuse of an accurate prediction model from 2017 quickly becomes inaccurate when Linux evolves, up to a 32% mean error by August 2020. We thus propose a new approach for transfer evolution-aware model shifting (TEAMS). It leverages the structure of a configurable system to transfer an initial predictive model towards its future versions with a minimal amount of extra processing for each version. We show that TEAMS vastly outperforms state of the art approaches over the 3 years history of Linux kernels, from 4.13 to 5.8.
Feature toggling is a technique for enabling branching-in-code. It is increasingly used during continuous deployment to incrementally test and integrate new features before their release. In principle, feature toggles tend to be light, that is, they are defined as simple Boolean flags and used in conditional statements to condition the activation of some software features. However, there is a lack of knowledge on whether and how they may interact with each other, in that case their enabling and testing become complex. We argue that finding the interactions of feature toggles is valuable for developers to know which of them should be enabled at the same time, which are impacted by a removed toggle, and to avoid their mis-configurations. In this work, we mine feature toggles and their interactions in five open-source projects. We then analyse how they are realized and whether they tend to be multiplied over time. Our results show that 7% of feature toggles interact with each other, 33% of them interact with another code expression, and their interactions tend to increase over time (22%, on average). Further, their interactions are expressed by simple logical operators (i.e., and and or) and nested if statements. We propose to model them into a Feature Toggle Model, and believe that our results are helpful towards robust management approaches of feature toggles. CCS CONCEPTS• Software and its engineering → Maintaining software.
Many research studies predict the performance of configurable software using machine learning techniques, thus requiring large amounts of data. Transfer learning aims to reduce the amount of data needed to train these models and has been successfully applied on different executing environments (hardware) or software versions. In this paper we investigate for the first time the idea of applying transfer learning between distinct configurable systems. We design a study involving two video encoders (namely x264 and x265) coming from different code bases. Our results are encouraging since transfer learning outperforms traditional learning for two performance properties (out of three). We discuss the open challenges to overcome for a more general application.
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