How Software Refactoring Impacts Execution Time

Refactoring aims at improving the maintainability of source code without modifying its external behavior. Previous works proposed approaches to recommend refactoring solutions to software developers. The generation of the recommended solutions is guided by metrics acting as proxy for maintainability (e.g., number of code smells removed by the recommended solution). These approaches ignore the impact of the recommended refactorings on other non-functional requirements, such as performance, energy consumption, and so forth. Little is known about the impact of refactoring operations on non-functional requirements other than maintainability. We aim to fill this gap by presenting the largest study to date to investigate the impact of refactoring on software performance, in terms of execution time. We mined the change history of 20 systems that defined performance benchmarks in their repositories, with the goal of identifying commits in which developers implemented refactoring operations impacting code components that are exercised by the performance benchmarks. Through a quantitative and qualitative analysis, we show that refactoring operations can significantly impact the execution time. Indeed, none of the investigated refactoring types can be considered “safe” in ensuring no performance regression. Refactoring types aimed at decomposing complex code entities (e.g., Extract Class/Interface, Extract Method) have higher chances of triggering performance degradation, suggesting their careful consideration when refactoring performance-critical code.

Development and Performance Measurement of an Affordable Unmanned Surface Vehicle (USV)

Indonesia is a maritime country that has vast coastal resources and biodiversity. To support the Indonesian maritime program, a topography mapping tool is needed. The ideal topography mapping tool is the Unmanned Surface Vehicle (USV). This paper proposes the design, manufacture, and development of an affordable autonomous USV. The USV which is composed of thruster and rudder is quite complicated to build. This study employs rudderless and double thrusters as the main actuators. PID compensator is utilized as the feedback control for the autonomous USV. Energy consumption is measured when the USV is in autonomous mode. The Dynamics model of USV was implemented to study the roll stability of the proposed USV. Open-source Mission Planner software was selected as the Ground Control Station (GCS) software. Performance tests were carried out by providing the USV with an autonomous mission to follow a specific trajectory. The results showed that the developed USV was able to complete autonomous mission with relatively small errors, making it suitable for underwater topography mapping.

A Study on Framework for Anti-Pattern Evaluation of System Software

Studies with a variety of viewpoints, goals, measurements, and quality characteristics have been conducted in order to determine the effect of design patterns on quality attributes. This has resulted in findings that are contradictory and difficult to compare. They want to explain these findings by taking into account confounding variables, practises, measurements, and implementation problems that have an impact on quality. Furthermore, there is a paucity of research that establishes a link between design pattern assessments and pattern creation studies, which is a significant limitation. For the purpose of detecting and categorising software performance anti-patterns, this article proposes a non-intrusive machine learning method dubbed Non-intrusive Performance Anti-pattern Detector (NiPAD). Keywords: software performance, anti-patterns, classification, machine learning, dynamic software analysis

Pengejawantahan Pendekatan Storytelling dalam Pemahaman Membangun Diagram Alir Data

Throughout the System Development Life Cycle (SDLC), establishment the software through various phases. System design is one of the most crucial aspects of the SDLC. The Data Flow Diagram (DFD) is a model to build the system. The purpose of the research was to make it easier for people to comprehend the diagram by carefully preparing DFD, then it created software according to the DFD design. Storytelling is the method employed, and it pertains to qualitative ways for creating language patterns. As a design reference, it needs to make the DFD pattern subsequently. The research mimics two scenarios to reflect previous research on the same principle. According to the findings of this study, there are two significant disparities. The first scenario concerns system user activity, whereas the second concerns software performance. Therefore, it corresponds to the core notion of input-process-output; narrative scenario 1 is more consistent and easier to design in the processed results created from sentence patterns to DFD patterns. Scenario 2, on the other hand, connects many process symbols to processes, complicating the DFD design. Finally, scenario 2 is beneficial for requirement's elicitation, whereas scenario 1 applies to business procedures.

A Prediction Method for Evolution Effects of Software Architecture

Software architecture evolution may lead to architecture erosion, resulting in the increase of software maintenance cost, the deterioration of software quality, the decline of software performance, and so on. In order to avoid software architecture erosion, we should evaluate the evolution effect of software architecture in time. This paper proposes a prediction method for the evolution effects of software architecture based on BP network. Firstly, this method proposes four evolution principles and evaluates the overall evolution effects based on the combined measurements. Then, we extract the evolutionary activities from release notes. Finally, we establish a prediction model for evolution effect based on BP network. Experimental results show that the proposed method can be used to predict the evolution effect.

Performance Variance Evaluation on Mozilla Firefox

<p>In order to evaluate software performance and find regressions, many developers use automated performance tests. However, the test results often contain a certain amount of noise that is not caused by actual performance changes in the programs. They are instead caused by external factors like operating system decisions or unexpected non-determinisms inside the programs. This makes interpreting the test results hard since results that differ from previous results cannot easily be attributed to either genuine changes or noise. In this thesis we use Mozilla Firefox as an example to try to find the causes for this performance variance, develop ways to reduce the noise and present a statistical technique that makes identifying genuine performance changes more reliable. Our results show that a significant amount of noise is caused by memory randomization and other external factors, that there is variance in Firefox internals that does not seem to be correlated with test result variance, and that our suggested statistical forecasting technique can give more reliable detection of genuine performance changes than the one currently in use by Mozilla.</p>

Performance Variance Evaluation on Mozilla Firefox

<p>In order to evaluate software performance and find regressions, many developers use automated performance tests. However, the test results often contain a certain amount of noise that is not caused by actual performance changes in the programs. They are instead caused by external factors like operating system decisions or unexpected non-determinisms inside the programs. This makes interpreting the test results hard since results that differ from previous results cannot easily be attributed to either genuine changes or noise. In this thesis we use Mozilla Firefox as an example to try to find the causes for this performance variance, develop ways to reduce the noise and present a statistical technique that makes identifying genuine performance changes more reliable. Our results show that a significant amount of noise is caused by memory randomization and other external factors, that there is variance in Firefox internals that does not seem to be correlated with test result variance, and that our suggested statistical forecasting technique can give more reliable detection of genuine performance changes than the one currently in use by Mozilla.</p>