Definition, implementation and validation of energy code smells: an exploratory study on an embedded system

Optimizing software in terms of energy efficiency is one of the challenges that both research and industry will have to face in the next few years.We consider energy efficiency as a software product quality characteristic, to be improved through the refactoring of appropriate code pattern: the aim of this work is identifying those code patterns, hereby defined as Energy Code Smells, that might increase the impact of software over power consumption. For our purposes, we perform an experiment consisting in the execution of several code patterns on an embedded system. These code patterns are executed in two versions: the first one contains a code issue that could negatively impact power consumption, the other one is refactored removing the issue. We measure the power consumption of the embedded device during the execution of each code pattern. We also track the execution time to investigate whether Energy Code Smells are also Performance Smells. Our results show that some Energy Code Smells actually have an impact over power consumption in the magnitude order of micro Watts. Moreover, those Smells did not introduce a performance decreas

Predicting the Impact of Batch Refactoring Code Smells on Application Resource Consumption

Automated batch refactoring has become a de-facto mechanism to restructure software that may have significant design flaws negatively impacting the code quality and maintainability. Although automated batch refactoring techniques are known to significantly improve overall software quality and maintainability, their impact on resource utilization is not well studied. This paper aims to bridge the gap between batch refactoring code smells and consumption of resources. It determines the relationship between software code smell batch refactoring, and resource consumption. Next, it aims to design algorithms to predict the impact of code smell refactoring on resource consumption. This paper investigates 16 code smell types and their joint effect on resource utilization for 31 open source applications. It provides a detailed empirical analysis of the change in application CPU and memory utilization after refactoring specific code smells in isolation and in batches. This analysis is then used to train regression algorithms to predict the impact of batch refactoring on CPU and memory utilization before making any refactoring decisions. Experimental results also show that our ANN-based regression model provides highly accurate predictions for the impact of batch refactoring on resource consumption. It allows the software developers to intelligently decide which code smells they should refactor jointly to achieve high code quality and maintainability without increasing the application resource utilization. This paper responds to the important and urgent need of software engineers across a broad range of software applications, who are looking to refactor code smells and at the same time improve resource consumption. Finally, it brings forward the concept of resource aware code smell refactoring to the most crucial software applications

Rohelisema tarkvaratehnoloogia poole tarkvaraanalüüsi abil

Mobiilirakendused, mis ei tühjenda akut, saavad tavaliselt head kasutajahinnangud. Mobiilirakenduste energiatõhusaks muutmiseks on avaldatud mitmeid refaktoreerimis- suuniseid ja tööriistu, mis aitavad rakenduse koodi optimeerida. Neid suuniseid ei saa aga seoses energiatõhususega üldistada, sest kõigi kontekstide kohta ei ole piisavalt energiaga seotud andmeid. Olemasolevad energiatõhususe parandamise tööriistad/profiilid on enamasti prototüübid, mis kohalduvad ainult väikese alamhulga energiaga seotud probleemide suhtes. Lisaks käsitlevad olemasolevad suunised ja tööriistad energiaprobleeme peamiselt a posteriori ehk tagantjärele, kui need on juba lähtekoodi sees. Android rakenduse koodi saab põhijoontes jagada kaheks osaks: kohandatud kood ja korduvkasutatav kood. Kohandatud kood on igal rakendusel ainulaadne. Korduvkasutatav kood hõlmab kolmandate poolte teeke, mis on rakendustesse lisatud arendusprotessi kiirendamiseks. Alustuseks hindame mitmete lähtekoodi halbade lõhnade refaktoreerimiste energiatarbimist Androidi rakendustes. Seejärel teeme empiirilise uuringu Androidi rakendustes kasutatavate kolmandate osapoolte võrguteekide energiamõju kohta. Pakume üldisi kontekstilisi suuniseid, mida võiks rakenduste arendamisel kasutada. Lisaks teeme süstemaatilise kirjanduse ülevaate, et teha kindlaks ja uurida nüüdisaegseid tugitööriistu, mis on rohelise Androidi arendamiseks saadaval. Selle uuringu ja varem läbi viidud katsete põhjal toome esile riistvarapõhiste energiamõõtmiste jäädvustamise ja taasesitamise probleemid. Arendame tugitööriista ARENA, mis võib aidata koguda energiaandmeid ja analüüsida Androidi rakenduste energiatarbimist. Viimasena töötame välja tugitööriista REHAB, et soovitada arendajatele energiatõhusaid kolmanda osapoole võrguteekeMobile apps that do not drain the battery usually get good user ratings. To make mobile apps energy efficient many refactoring guidelines and tools are published that help optimize the app code. However, these guidelines cannot be generalized w.r.t energy efficiency, as there is not enough energy-related data for every context. Existing energy enhancement tools/profilers are mostly prototypes applicable to only a small subset of energy-related problems. In addition, the existing guidelines and tools mostly address the energy issues a posteriori, i.e., once they have already been introduced into the code. Android app code can be roughly divided into two parts: the custom code and the reusable code. Custom code is unique to each app. Reusable code includes third-party libraries that are included in apps to speed up the development process. We start by evaluating the energy consumption of various code smell refactorings in native Android apps. Then we conduct an empirical study on the energy impact of third-party network libraries used in Android apps. We provide generalized contextual guidelines that could be used during app development Further, we conduct a systematic literature review to identify and study the current state of the art support tools available to aid green Android development. Based on this study and the experiments we conducted before, we highlight the problems in capturing and reproducing hardware-based energy measurements. We develop the support tool ‘ARENA’ that could help gather energy data and analyze the energy consumption of Android apps. Last, we develop the support tool ‘REHAB’ to recommend energy efficient third-party network libraries to developers.https://www.ester.ee/record=b547174

Reducing energy usage in resource-intensive Java-based scientific applications via micro-benchmark based code refactorings

In-silico research has grown considerably. Today's scientific code involves long-running computer simulations and hence powerful computing infrastructures are needed. Traditionally, research in high-performance computing has focused on executing code as fast as possible, while energy has been recently recognized as another goal to consider. Yet, energy-driven research has mostly focused on the hardware and middleware layers, but few efforts target the application level, where many energy-aware optimizations are possible. We revisit a catalog of Java primitives commonly used in OO scientific programming, or micro-benchmarks, to identify energy-friendly versions of the same primitive. We then apply the micro-benchmarks to classical scientific application kernels and machine learning algorithms for both single-thread and multi-thread implementations on a server. Energy usage reductions at the micro-benchmark level are substantial, while for applications obtained reductions range from 3.90% to 99.18%.Fil: Longo, Mathias. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina. University of Southern California; Estados UnidosFil: Rodriguez, Ana Virginia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

Test Smell: A Parasitic Energy Consumer in Software Testing

Traditionally, energy efficiency research has focused on reducing energy consumption at the hardware level and, more recently, in the design and coding phases of the software development life cycle. However, software testing's impact on energy consumption did not receive attention from the research community. Specifically, how test code design quality and test smell (e.g., sub-optimal design and bad practices in test code) impact energy consumption has not been investigated yet. This study examined 12 Apache projects to analyze the association between test smell and its effects on energy consumption in software testing. We conducted a mixed-method empirical analysis from two dimensions; software (data mining in Apache projects) and developers' views (a survey of 62 software practitioners). Our findings show that: 1) test smell is associated with energy consumption in software testing. Specifically smelly part of a test case consumes 10.92\% more energy compared to the non-smelly part. 2) certain test smells are more energy-hungry than others, 3) refactored test cases tend to consume less energy than their smelly counterparts, and 4) most developers lack knowledge about test smells' impact on energy consumption. We conclude the paper with several observations that can direct future research and developments