面向对象软件内聚度度量数据分布的实证研究

度量数据的分布信息对于理解和使用面向对象软件度量有重要意义.人们对面向对象软件规模度量、耦合度度量乃至继承维度的度量数据的分布都有研究,但对除内聚度缺乏度LCOM之外的内聚度度量数据的分布却缺乏研究.已有的实证研究表明,LCOM并不是好的内聚度度量,因此探讨其他内聚度度量数据分布很有必要.对包括内聚度缺乏度、基于连通性的内聚度度量和基于相似性的内聚度度量总共17个度量指标在112个Java开源软件项目的分布情况进行实证研究,对每个度量指标的每个项目数据使用幂律分布和对数正态分布进行拟合,并使用荟萃分析方法对拟合结果进行了分析.实证研究结果表明,非规范化的内聚度量可使用对数正态分布和幂律分布拟合,但规范化的基于相似性的内聚度量（包括CC、LSCC、SCOM和SCC）需要排除方法数小于等于1或字段数为0的特殊类才能使用对数正态分布拟合,而基于连通性的内聚度度量（包括TCC、LCC、DCD和DCI）则只有对应的非规范化版本的数据才符合对数正态分布或幂律分布.实证研究可帮助人们更好地理解和使用内聚度度量,特别是可以帮助人们如何利用已有的方法确定内聚度度量的阈值.     

软件能耗优化技术研究进展

为了设计高性能低能耗的系统,需要从硬件设计和软件设计两个方面进行考虑,以取得性能和能耗的最佳权衡.研究利用软件技术降低系统能耗的问题,主要探讨系统开发阶段的低能耗软件优化与评估技术.优化技术包括指令级优化、算法级优化与软件体系结构优化3类,阐述在各类优化技术研究中面临的问题和当前的研究工作进展;深入讨论了低能耗软件优化的关键支撑技术——软件能耗估算,指出并分析面向处理器和面向全系统的软件能耗估算面临的主要问题和研究进展;最后展望进一步研究的主要问题和发展趋势.     

An effective speedup metric for measuring productivity in large-scale parallel computer systems

With the parallel computer systems scaling-up, the measure index for performance of the systems demands a shift from traditional “high performance” to “high productivity.” This brings a new challenge to defining a synthetic, yet meaningful, measure index of multiple productivity variables; namely computing performance, reliability, energy consumption, parallel software development, etc. Traditional measures for large-scale parallel computer systems merely focus on computing performance, and are incapable of measuring the multiple productivity variables simultaneously in an effective manner. A recently proposed market-related money model, which pursues high utility/cost ratio, relies on money as a measure to consider the multiple productivity variables. Differing from the previous models, this paper proposes a novel system productivity speedup metric for large-scale parallel computer systems. The metric uses speedup instead of money to comprehensively unify the measures of multiple productivity variables. Finally, we propose a trade-off productivity measurement to weigh different productivity variables, to address different design targets. The measurement can facilitate the system evaluation, expose future technique tendencies, and guide future system design.     

能耗并行加速比:高性能计算系统综合性能的有效度量

随着并行系统规模的扩大,高性能计算系统运行时消耗的能耗也在急剧增长,过高的能耗也给系统的可靠性、稳定性等方面带来严峻挑战。在这种情形下,能耗问题受到了前所未有的关注。因此,设计和研究高性能计算系统,需要在考虑高计算性能的同时兼顾系统低能耗的要求,这为高性能计算系统的度量模型提出了新的挑战。于是,大规模并行系统逐渐从"高性能"走向"高效能"的衡量标准。基于此,本文采用加速比度量指标,从系统可扩展角度将计算性能和能量消耗要素进行综合,提出了一种度量高性能计算系统综合性能的能耗并行加速比模型。该模型能够直观地反映并行计算系统的效能,旨在指导系统设计和应用研究。最后,通过对该模型的分析和模拟,验证了模型的有效性。     

An early-stage statement-level metric for energy characterization of embedded processors

This work presents an early stage statement-level metric for energy characterization of embedded processors. Definition and the framework for metric evaluation are provided. In particular, such a metric is based on an existing assembly-level analysis and some profiling activities performed on a given C benchmark, and it is related to the average energy consumption of a generic C statement, for a given target processor. Its evaluation is performed with a one-time effort and, once available, it can be used to rapidly estimate the energy consumption of a given C function for all the considered processors. Two reference embedded processors are then considered in order to show an example of usage of the proposed metric and framework.     

基于AADL语言的移动软件能耗评估方法

移动软件往往部署在电量受限的处理器上,能耗已成为评价这类软件的一个重要质量属性.与代码级和指令级相比,在设计级进行能耗评估具有耗时短、成本低的优点,近年来已成为软件工程学术界和工业界的研究热点.目前虽已涌现出一些设计级能耗评估方法,但这些方法大多未对软件构件的内部行为元素进行能耗评估,导致了精度问题.针对上述问题,基于体系结构分析设计语言AADL和StrongARM处理器构建了一种移动软件能耗评估模型,进一步定义了面向AADL语言的移动软件能耗评估过程,在此基础上研发了一款能耗评估工具,进而提出一种基于AADL语言的移动软件能耗评估方法.实验结果表明该方法较已有AADL能耗评估方法在精度上有所提高.     

面向数据中心的服务器能耗模型综述

伴随着云计算技术的快速发展,数据中心的服务器能耗日益激增,带来了严重的经济和环境问题,降低数据中心能耗,对缩减数据中心运营成本、实现全球“双碳”战略目标具有重要意义。因此,不同层面的服务器能耗模型构建和预估成为了近年来研究的热点。据此,从硬件、软件层面系统地总结了服务器能耗模型的相关工作。在硬件层面,对服务器的整体能耗按加法模型、基于系统利用率模型和其他模型分类；同时,还总结了服务器部件粒度的能耗模型,涵盖CPU、内存、磁盘和网络接口。在软件层面,按机器学习的类别将服务器能耗模型归纳为监督学习、非监督学习、强化学习。此外,还比较了不同能耗模型的优缺点、适用场景,展望了能耗模型的未来研究方向。     

Comparing system level power management policies

Reducing power consumption is a challenge to system designers. Portable systems, such as laptop computers and personal digital assistants (PDAs), draw power from batteries, so reducing power consumption extends their operating times. For desktop computers or servers, high power consumption raises temperature and deteriorates performance and reliability. Soaring energy prices and rising concern about the environmental impact of electronics systems further highlight the importance of low power consumption. Power reduction techniques can be classified as static and dynamic. Static techniques, such as synthesis and compilation for low power, are applied at design time. In contrast, dynamic techniques use runtime behavior to reduce power when systems are serving light workloads or are idle. These techniques are known as dynamic power management (DPM). DPM can be achieved in different ways; for example, dynamic voltage scaling (DVS) changes supply voltage at runtime as a method of power management. Here, we use DPM specifically for shutting down unused I/O devices. We built an experimental environment on a laptop computer running Microsoft Windows. We implemented existing power management policies and quantitatively compared their effects on power saving and performance degradation     

嵌入式系统源程序级软件能耗建模与分析

针对嵌入式系统能耗对各种嵌入式设备工作时长的影响,本文从系统指令级到源程序级的软件能耗考虑,首先通过分析设备源程序级语句的相关特征,基于源程序语句的指令能耗,提出一种针对源程序级的能耗模型,然后基于模型分析对五个经典算法的源程序中不同类别语句进行能耗优化,最后分别对五组经典算法优化前后的能耗比较。实验表明,本模型使得优化后的源程序能耗降低了9.46%-50.29%,达到了降低嵌入式系统软件能耗的目的。     

软件体系结构评估技术

作为在软件生命周期早期保障软件质量的重要手段之一,软件体系结构评估技术是软件体系结构研究中的一个重要组成部分.将现有的软件体系结构评估方法划分为3类:基于场景的评估方法、基于度量和预测的评估方法以及特定软件体系结构描述语言的评估方法.按照软件体系结构评估技术的评价框架,分别从评估方法的目标、质量属性、关键技术等方面对这3类方法的特点进行介绍和对比.最后分析了现有研究中存在的不足并进一步探讨了软件体系结构评估技术的研究趋势.     

基于概率的无监督缺陷预测方法

软件缺陷预测能够提高软件开发和测试的效率,保障软件质量。无监督缺陷预测方法具有不需要标签数据的特点,从而能够快速应用于工程实践中。提出了基于概率的无监督缺陷预测方法—PCLA,将度量元值与阈值的差值映射为概率,使用概率评估类存在缺陷的可能性,然后再通过聚类和标记来完成缺陷预测,以解决现有无监督方法直接根据阈值判断时对阈值比较敏感而引起的信息丢失问题。将PCLA方法应用在NetGen和Relink两组数据集,共7个软件项目上,实验结果表明PCLA方法在查全率、查准率、F-measure上相对现有无监督方法分别平均提升4.1%、2.52%、3.14%。     

用于软件选型的度量模型及系统实现

针对现有软件选型手段存在的缺乏统一标准、主观性和片面性较强的问题,建立了软件选型过程模型,设计了基于模糊综合评价的软件选型度量模型,讨论了软件选型度量模型的三个层次,进而开发了可操作的软件选型度量原型系统,并通过实验验证了该系统的有效性。     

SEProf: A high-level software energy profiling tool for an embedded processor enabling power management functions

Energy efficiency has become one of the most important design issues for embedded systems. To examine the power consumption of an embedded system, an energy profiling tool is highly demanded. Although a number of energy profiling tools have been proposed, they are not directly applicable to the embedded processors with power management functions that are widely utilized in battery-operated embedded systems to reduce power consumption. Hence, this study presents a high-level energy profiling tool, called SEProf, that estimates the energy consumption of an embedded system running multithread software and a multitasking operating system (OS) that supports power management functions. This study implements the proposed SEProf in Linux 2.6.19 and evaluates its performance on an ARM11 MPCore processor. Experimental results demonstrate that the proposed tool can provide accurate energy profiling results with a low profiling overhead.     

面向移动Agent的软件度量工具的设计

利用软件度量工具对软件的各类质量属性度量,对于提高程序的质量有重要意义。在分析面向移动Agent的度量指标的基础上,设计并实现一种基于移动Agent的软件度量工具,该工具通过度量Agent与系统中其它Agent进行交互来获取度量所需信息并对其进行加工处理,用户可以通过度量Agent提供的接口查询度量的结果。最后给出度量指标与度量特征之间关系的实验结果。     

面向移动Agent的软件度量工具的设计

利用软件度量工具对软件的各类质量属性度量,对于提高程序的质量有重要意义.在分析面向移动Agent的度量指标的基础上,设计并实现一种基于移动Agent的软件度量工具,该工具通过度量Agent与系统中其它Agent进行交互来获取度量所需信息并对其进行加工处理,用户可以通过度量Agent提供的接口查询度量的结果.最后给出度量指标与度量特征之间关系的实验结果.     

ConsilientSFL: using preferential voting system to generate combinatorial ranking metrics for spectrum-based fault localization

Spectrum-based fault localization (SFL) techniques have shown considerable effectiveness in localizing software faults. They leverage a ranking metric to automatically assign suspiciousness scores to certain entities in a given faulty program. However, for some programs, the current SFL ranking metrics lose effectiveness. In this paper, we introduce ConsilientSFL that is served to synthesize a new ranking metric for a given program, based on a customized combination of a set of given ranking metrics. ConsilientSFL can be significant since it demonstrates the usage of voting systems into a software engineering task. First, several mutated, faulty versions are generated for a program. Then, the mutated versions are executed with the test data. Next, the effectiveness of each existing ranking metric is computed for each mutated version. After that, for each mutated version, the computed existing metrics are ranked using a preferential voting system. Consequently, several top metrics are chosen based on their ranks across all mutated versions. Finally, the chosen ranking metrics are normalized and synthesized, yielding a new ranking metric. To evaluate ConsilientSFL, we have conducted experiments on 27 subject programs from Code4Bench and Siemens benchmarks. In the experiments, we found that ConsilientSFL outperformed every single ranking metric. In particular, for all programs on average, we have found performance measures recall, precision, f-measure, and percentage of code inspection, to be nearly 7, 9, 12, and 5 percentages larger than using single metrics, respectively. The impact of this work is twofold. First, it can mitigate the issue with the choice and usage of a proper ranking metric for the faulty program at hand. Second, it can help debuggers find more faults with less time and effort, yielding higher quality software.

     

Exploring optimal combination of a file system and an I/O scheduler for underlying solid state disks

Performance and energy consumption of a solid state disk （SSD） highly depend on file systems and I/O schedulers in operating systems. To find an optimal combination of a file system and an I/O scheduler for SSDs, we use a metric called the aggregative indicator （AI）, which is the ratio of SSD performance value （e.g., data transfer rate in MB/s or throughput in IOPS） to that of energy consumption for an SSD. This metric aims to evaluate SSD performance per energy consumption and to study the SSD which delivers high performance at low energy consumption in a combination of a file system and an I/O scheduler. We also propose a metric called Cemp to study the changes of energy consumption and mean performance for an Intel SSD （SSD-I） when it provides the largest AI, lowest power, and highest performance, respectively. Using Cemp, we attempt to find the combination of a file system and an I/O scheduler to make SSD-I deliver a smooth change in energy consumption. We employ Filebench as a workload generator to simulate a wide range of workloads （i.e., varmail, fileserver, and webserver）, and explore optimM combinations of file systems and I/O schedulers （i.e., optimal values of AI） for tested SSDs under different workloads. Experimental results reveal that the proposed aggregative indicator is comprehensive for exploring the optimal combination of a file system and an I/O scheduler for SSDs, compared with an individual metric.     

Energy Efficient Scheduling of Real-Time Tasks on Multicore Processors

Multicore processors deliver a higher throughput at lower power consumption than unicore pro- cessors. In the near future, they will thus be widely used in mobile real-time systems. There have been many research on energy-efficient scheduling of real-time tasks using DVS. These approaches must be modified for multicore processors, however, since normally all the cores in a chip must run at the same performance level. Thus blindly adopting existing DVS algorithms which do not consider the restriction will result in a waste of energy. This article suggests Dynamic Repartitioning algorithm based on existing partitioning approaches of multiprocessor systems. The algorithm dynamically balances the task loads of multiple cores to optimize power consumption during execution. We also suggest Dynamic Core Scaling algorithm which adjusts the number of active cores to reduce leakage power consumption under low load conditions. Simulation results show that Dynamic Repartitioning can produce energy savings of about 8% even with the best energy-efficient partitioning algorithm. The results also show that Dynamic Core Scaling can reduce energy consumption by about 26% under low load conditions.     

基于ZEUS的主体度量

在软件工程领域，主体系统的设计和开发受到越来越多的关注。但是传统的软件度量技术和面向对象的软件度量技术不适用于对主体系统的分析，而影响主体系统质量的主要因素是复杂度和知识能力。文中结合ZEUS主体系统，采用FSM框架给出主体复杂度和知识能力的度量指标以及度量主体。