94 research outputs found
Technical Debt Management Automation: State of the Art and Future Perspectives
Technical Debt (TD) refers to non-optimal decisions made in software projects
that may lead to short-term benefits, but potentially harm the system's
maintenance in the long-term. Technical debt management (TDM) refers to a set
of activities that are performed to handle TD, e.g., identification. These
activities can entail tasks such as code and architectural analysis, which can
be time-consuming if done manually. Thus, substantial research work has focused
on automating TDM tasks (e.g., automatic identification of code smells).
However, there is a lack of studies that summarize current approaches in TDM
automation. This can hinder practitioners in selecting optimal automation
strategies to efficiently manage TD. It can also prevent researchers from
understanding the research landscape and addressing the research problems that
matter the most. Thus, the main objective of this study is to provide an
overview of the state of the art in TDM automation, analyzing the available
tools, their use, and the challenges in automating TDM. For this, we conducted
a systematic mapping study (SMS), and from an initial set of 1086 primary
studies, 178 were selected to answer three research questions covering
different facets of TDM automation. We found 121 automation artifacts, which
were classified in 4 different types (i.e., tools, plugins, scripts, and bots);
the inputs/outputs and interfaces were also collected and reported. Finally, a
conceptual model is proposed that synthesizes the results and allows to discuss
the current state of TDM automation and related challenges. The results show
that the research community has investigated to a large extent how to perform
various TDM activities automatically, considering the number of studies and
automation artifacts we identified. More research is needed towards fully
automated TDM, specially concerning the integration of the automation
artifacts
Simulating Systems-of-Systems Dynamic Architectures
Systems-of-Systems (SoS) combine heterogeneous, independent systems to offer complex functionalities for highly dynamic smart applications. Due to their critical nature, SoS should be reliable and work without interruption that could cause serious losses. SoS architectural design can facilitate the prediction of the impact of failures due to SoS behavior. However, existing approaches do not support such evaluation. The main contribution of this paper is to present Dynamic-SoS, an approach to predict, at design time, the SoS architectural behavior at runtime to evaluate whether the SoS can sustain their operation. Results of our multiple case studies reveal Dynamic-SoS is a promising approach that could contribute to the quality of SoS by reliably enabling prior assessment of their dynamic architecture
DmS-Modeler: A Tool for Modeling Decision-making Systems for Self-adaptive Software Domain
Abstract-The ability to modify its own structure and/or behavior at runtime is a native feature in the development of Self-adaptive Software (SaS). In previous work, a Reference Architecture for SaS (RA4SaS), an automated process for adaptation, and a framework for decision-making were developed to assist the development of SaS. Although such initiatives have collaborated with evolution of SaS, the design of the Decisionmaking Systems (DmS), element of first class for SaS, is manually conducted. Therefore, this paper presents a tool called DmSModeler, which aims to assist the development of DmS for SaS, providing facilities for modeling, calibration of such system, and automatic generation of infrastructure (i.e., source code and databases). Aiming to present the applicability of our tool, a case study was conducted and the results enable us to have good perspectives of contribution to the SaS area and other domains of software systems
Fazendas Históricas Paulistas Dos Séculos Xviii E Xvx: Premissas Teóricas E Metodológicas Para Inventariar Bens Patrimoniais
Os bens patrimoniais das fazendas paulistas,dos séculos XVIII e XIX, constituem-se emimportantes fontes para ensino, pesquisa eturismo. No contexto do projeto “PatrimônioCultural Rural Paulista: Espaço Privilegiadopara Pesquisa, Educação e Turismo”, apresentaseuma proposta teórico-metodológica parainventariar bens patrimoniais.182037-5
Investigating the effect of design patterns on energy consumption
Gang of Four (GoF) patterns are well-known best practices for the design of object-oriented systems. In this paper, we aim at empirically assessing their relationship to energy consumption, ie, a performance indicator that has recently attracted the attention of both researchers and practitioners. To achieve this goal, we investigate pattern-participating methods (ie, those that play a role within the pattern) and compare their energy consumption to the consumption of functionally equivalent alternative (nonpattern) solutions. We obtained the alternative solution by refactoring the pattern instances using well-known transformations (eg, replace polymorphism with conditional statements). The comparison is performed on 169 methods of 2 GoF patterns (namely, State/Strategy and Template Method), retrieved from 2 well-known open source projects. The results suggest that for the majority of cases the alternative design excels in terms of energy consumption. However, in some cases (eg, when the method is large in size or invokes many methods) the pattern solution presents similar or lower energy consumption. The outcome of our study can be useful to both researchers and practitioners, because we: (1) provide evidence on a possible negative effect of GoF patterns, and (2) can provide guidance on which cases the use of the pattern is not hurting energy consumption
S.O.B (Save Our Budget) - A Simulation-Based Method for Prediction of Acquisition Costs of Constituents of a System-of-Systems
Software economics, acquisition, and pricing are important concerns for Systems-of-Systems (SoS). SoS are alliances of independent software-intensive systems combined to offer holistic functionalities as a result of the constituents interoperability. SoS engineering involves separately acquiring constituents and combining them to form the SoS. Despite the existence of cost prediction techniques, predicting SoS acquisition costs at design-time should also include the analysis of different suppliers of constituents, their respective prices and quality. However, known methods cover only two out of these three parameters. The main contribution of this article is to present the S.O.B. (Save Our Budget) method, a novel simulation-based method to predict, at design-time, the acquisition cost of constituents, while still considering quality attributes and different suppliers. Results of a case study in the Smart Building domain revealed that S.O.B. method supports a precise prediction of acquisition cost of constituents to build a SoS for that domain. Furthermore, it also contributes to estimate the cost based on a pre-established quality attribute (functional suitability), as well as to support the selection of coalition that exhibits better results through the analysis of cost-benefit ratio.Software economics, acquisition, and pricing are important concerns for Systems-of-Systems (SoS). SoS are alliances of independent software-intensive systems combined to offer holistic functionalities as a result of the constituents interoperability. SoS engineering involves separately acquiring constituents and combining them to form the SoS. Despite the existence of cost prediction techniques, predicting SoS acquisition costs at design-time should also include the analysis of different suppliers of constituents, their respective prices and quality. However, known methods cover only two out of these three parameters. The main contribution of this article is to present the S.O.B. (Save Our Budget) method, a novel simulation-based method to predict, at design-time, the acquisition cost of constituents, while still considering quality attributes and different suppliers. Results of a case study in the Smart Building domain revealed that S.O.B. method supports a precise prediction of acquisition cost of constituents to build a SoS for that domain. Furthermore, it also contributes to estimate the cost based on a pre-established quality attribute (functional suitability), as well as to support the selection of coalition that exhibits better results through the analysis of cost-benefit ratio
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