481 research outputs found
A heuristic-based approach to code-smell detection
Encapsulation and data hiding are central tenets of the object oriented paradigm. Deciding what data and behaviour to form into a class and where to draw the line between its public and private details can make the difference between a class that is an understandable, flexible and reusable abstraction and one which is not. This decision is a difficult one and may easily result in poor encapsulation which can then have serious implications for a number of system qualities. It is often hard to identify such encapsulation problems within large software systems until they cause a maintenance problem (which is usually too late) and attempting to perform such analysis manually can also be tedious and error prone. Two of the common encapsulation problems that can arise as a consequence of this decomposition process are data classes and god classes. Typically, these two problems occur together – data classes are lacking in functionality that has typically been sucked into an over-complicated and domineering god class. This paper describes the architecture of a tool which automatically detects data and god classes that has been developed as a plug-in for the Eclipse IDE. The technique has been evaluated in a controlled study on two large open source systems which compare the tool results to similar work by Marinescu, who employs a metrics-based approach to detecting such features. The study provides some valuable insights into the strengths and weaknesses of the two approache
RefDiff: Detecting Refactorings in Version Histories
Refactoring is a well-known technique that is widely adopted by software
engineers to improve the design and enable the evolution of a system. Knowing
which refactoring operations were applied in a code change is a valuable
information to understand software evolution, adapt software components, merge
code changes, and other applications. In this paper, we present RefDiff, an
automated approach that identifies refactorings performed between two code
revisions in a git repository. RefDiff employs a combination of heuristics
based on static analysis and code similarity to detect 13 well-known
refactoring types. In an evaluation using an oracle of 448 known refactoring
operations, distributed across seven Java projects, our approach achieved
precision of 100% and recall of 88%. Moreover, our evaluation suggests that
RefDiff has superior precision and recall than existing state-of-the-art
approaches.Comment: Paper accepted at 14th International Conference on Mining Software
Repositories (MSR), pages 1-11, 201
Detection of microservice smells through static analysis
A arquitetura de microsserviços é um modelo arquitetural promissor na área de software,
atraindo desenvolvedores e empresas para os seus princípios convincentes. As suas vantagens
residem no potencial para melhorar a escalabilidade, a flexibilidade e a agilidade, alinhando se com as exigências em constante evolução da era digital. No entanto, navegar entre as
complexidades dos microsserviços pode ser uma tarefa desafiante, especialmente à medida
que este campo continua a evoluir.
Um dos principais desafios advém da complexidade inerente aos microsserviços, em que o seu
grande número e interdependências podem introduzir novas camadas de complexidade. Além
disso, a rápida expansão dos microsserviços, juntamente com a necessidade de aproveitar as
suas vantagens de forma eficaz, exige uma compreensão mais profunda das potenciais
ameaças e problemas que podem surgir. Para tirar verdadeiramente partido das vantagens
dos microsserviços, é essencial enfrentar estes desafios e garantir que o desenvolvimento e a
adoção de microsserviços sejam bem-sucedidos.
O presente documento pretende explorar a área dos smells da arquitetura de microsserviços
que desempenham um papel tão importante na dívida técnica dirigida à área dos
microsserviços.
Embarca numa exploração de investigação abrangente, explorando o domínio dos smells de
microsserviços. Esta investigação serve como base para melhorar um catálogo de smells de
microsserviços. Esta investigação abrangente obtém dados de duas fontes primárias:
systematic mapping study e um questionário a profissionais da área. Este último envolveu 31
profissionais experientes com uma experiência substancial no domínio dos microsserviços.
Além disso, são descritos o desenvolvimento e o aperfeiçoamento de uma ferramenta
especificamente concebida para identificar e resolver problemas relacionados com os
microsserviços. Esta ferramenta destina-se a melhorar o desempenho dos programadores
durante o desenvolvimento e a implementação da arquitetura de microsserviços.
Por último, o documento inclui uma avaliação do desempenho da ferramenta. Trata-se de
uma análise comparativa efetuada antes e depois das melhorias introduzidas na ferramenta.
A eficácia da ferramenta será avaliada utilizando o mesmo benchmarking de microsserviços
utilizado anteriormente, para além de outro benchmarking para garantir uma avaliação
abrangente.The microservices architecture stands as a beacon of promise in the software landscape,
drawing developers and companies towards its compelling principles. Its appeal lies in the
potential for improved scalability, flexibility, and agility, aligning with the ever-evolving
demands of the digital age. However, navigating the intricacies of microservices can be a
challenging task, especially as this field continues to evolve.
A key challenge arises from the inherent complexity of microservices, where their sheer
number and interdependencies can introduce new layers of intricacy. Furthermore, the rapid
expansion of microservices, coupled with the need to harness their advantages effectively,
demands a deeper understanding of the potential pitfalls and issues that may emerge. To
truly unlock the benefits of microservices, it is essential to address these challenges head-on
and ensure a successful journey in the world of microservices development and adoption.
The present document intends to explore the area of microservice architecture smells that
play such an important role in the technical debt directed to the area of microservices.
It embarks on a comprehensive research exploration, delving into the realm of microservice
smells. This research serves as the cornerstone for enhancing a microservice smell catalogue.
This comprehensive research draws data from two primary sources: a systematic mapping
research and an industry survey. The latter involves 31 seasoned professionals with
substantial experience in the field of microservices.
Moreover, the development and enhancement of a tool specifically designed to identify and
address issues related to microservices is described. This tool is aimed at improving
developers' performance throughout the development and implementation of microservices
architecture.
Finally, the document includes an evaluation of the tool's performance. This involves a
comparative analysis conducted before and after the tool's enhancements. The tool's
effectiveness will be assessed using the same microservice benchmarking as previously
employed, in addition to another benchmark to ensure a comprehensive evaluation
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
Property-Based Testing - The ProTest Project
The ProTest project is an FP7 STREP on property based testing. The purpose of the project is to develop software engineering approaches to improve reliability of service-oriented networks; support fault-finding and diagnosis based on specified properties of the system. And to do so we will build automated tools that will generate and run tests, monitor execution at run-time, and log events for analysis.
The Erlang / Open Telecom Platform has been chosen as our initial implementation vehicle due to its robustness and reliability within the telecoms sector. It is noted for its success in the ATM telecoms switches by Ericsson, one of the project partners, as well as for multiple other uses such as in facebook, yahoo etc. In this paper we provide an overview of the project goals, as well as detailing initial progress in developing property based testing techniques and tools for the concurrent functional programming language Erlang
PROGRAM INSPECTION AND TESTING TECHNIQUES FOR CODE CLONES AND REFACTORINGS IN EVOLVING SOFTWARE
Developers often perform copy-and-paste activities. This practice causes the similar code fragment (aka code clones) to be scattered throughout a code base. Refactoring for clone removal is beneficial, preventing clones from having negative effects on software quality, such as hidden bug propagation and unintentional inconsistent changes. However, recent research has provided evidence that factoring out clones does not always reduce the risk of introducing defects, and it is often difficult or impossible to remove clones using standard refactoring techniques. To investigate which or how clones can be refactored, developers typically spend a significant amount of their time managing individual clone instances or clone groups scattered across a large code base.
To address the problem, this research proposes two techniques to inspect and validate refactoring changes. First, we propose a technique for managing clone refactorings, Pattern-based clone Refactoring Inspection (PRI), using refactoring pattern templates. By matching the refactoring pattern templates against a code base, it summarizes refactoring changes of clones, and detects the clone instances not consistently factored out as potential anomalies. Second, we propose Refactoring Investigation and Testing technique, called RIT. RIT improves the testing efficiency for validating refactoring changes. RIT uses PRI to identify refactorings by analyzing original and edited versions of a program. It then uses the semantic impact of a set of identified refactoring changes to detect tests whose behavior may have been affected and modified by refactoring edits. Given each failed asserts, RIT helps developers focus their attention on logically related program statements by applying program slicing for minimizing each test. For debugging purposes, RIT determines specific failure-inducing refactoring edits, separating from other changes that only affect other asserts or tests
A large-scale empirical exploration on refactoring activities in open source software projects
Refactoring is a well-established practice that aims at improving the internal structure of a software system without changing its external behavior. Existing literature provides evidence of how and why developers perform refactoring in practice. In this paper, we continue on this line of research by performing a large-scale empirical analysis of refactoring practices in 200 open source systems. Specifically, we analyze the change history of these systems at commit level to investigate: (i) whether developers perform refactoring operations and, if so, which are more diffused and (ii) when refactoring operations are applied, and (iii) which are the main developer-oriented factors leading to refactoring. Based on our results, future research can focus on enabling automatic support for less frequent refactorings and on recommending refactorings based on the developer's workload, project's maturity and developer's commitment to the project
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