Feature Interaction Problem Research Articles

HP3D-V2V: High-Precision 3D Object Detection Vehicle-to-Vehicle Cooperative Perception Algorithm.

Cooperative perception in the field of connected autonomous vehicles (CAVs) aims to overcome the inherent limitations of single-vehicle perception systems, including long-range occlusion, low resolution, and susceptibility to weather interference. In this regard, we propose a high-precision 3D object detection V2V cooperative perception algorithm. The algorithm utilizes a voxel grid-based statistical filter to effectively denoise point cloud data to obtain clean and reliable data. In addition, we design a feature extraction network based on the fusion of voxels and PointPillars and encode it to generate BEV features, which solves the spatial feature interaction problem lacking in the PointPillars approach and enhances the semantic information of the extracted features. A maximum pooling technique is used to reduce the dimensionality and generate pseudoimages, thereby skipping complex 3D convolutional computation. To facilitate effective feature fusion, we design a feature level-based crossvehicle feature fusion module. Experimental validation is conducted using the OPV2V dataset to assess vehicle coperception performance and compare it with existing mainstream coperception algorithms. Ablation experiments are also carried out to confirm the contributions of this approach. Experimental results show that our architecture achieves lightweighting with a higher average precision (AP) than other existing models.

Feature Interactions on Steroids: On the Composition of ML Models

One of the key differences between traditional software engineering and machine learning (ML) is the lack of specifications for ML models. Traditionally, specifications provide a cornerstone for compositional reasoning and for the divide-and-conquer strategy of how we build large and complex systems from components, but these are hard to come by for machine learned components. While the lack of specification seems like a fundamental new problem at first sight, in fact, software engineers routinely deal with iffy specifications in practice. We face weak specifications, wrong specifications, and unanticipated interactions among specifications. ML may push us further, but the problems are not fundamentally new. Rethinking ML model composition from the perspective of the feature-interaction problem highlights the importance of software design.

AFGSL: Automatic Feature Generation based on Graph Structure Learning

Feature engineering relies on domain knowledge and human intervention. To automate the process of feature engineering, automated feature construction methods use deep neural networks to capture feature interactions and attention coefficients to quantify the relationship between features. However, these methods ignore the influence of insignificant features that introduce noise and degrade the performance of the model. In this paper, we study the problem of feature interactions from the perspective of graph and propose a novel Automatic Feature Generation model based on Graph Structure Learning, called AFGSL. In this model, the adjacency matrix reflects the relationships between features, so that it can be used to filter out insignificant features. Furthermore, Q-learning is used to train the policy of stacking interaction layers, which enables it to make full use of both local and global information in the process of feature generation. The results of experiments on four real-world datasets show that AFGSL outperforms the state-of-the-art methods.

FM-GRU: A Time Series Prediction Method for Water Quality Based on seq2seq Framework

Water quality prediction plays a crucial role in both enterprise management and government environmental management. However, due to the variety in water quality data, inconsistent frequency of data acquisition, inconsistency in data organization, and volatility and sparsity of data, predicting water quality accurately and efficiently has become a key problem. This paper presents a recurrent neural network water quality prediction method based on a sequence-to-sequence (seq2seq) framework. The gate recurrent unit (GRU) model is used as an encoder and decoder, and a factorization machine (FM) is integrated into the model to solve the problem of high sparsity and high dimensional feature interaction in the data, which was not addressed by the water quality prediction models in prior research. Moreover, due to the long period and timespan of water quality data, we add a dual attention mechanism to the seq2seq framework to address memory failures in deep learning. We conducted a series of experiments, and the results show that our proposed method is more accurate than several typical water quality prediction methods.

Feature Interaction for Streaming Feature Selection.

Traditional feature selection methods assume that all data instances and features are known before learning. However, it is not the case in many real-world applications that we are more likely faced with data streams or feature streams or both. Feature streams are defined as features that flow in one by one over time, whereas the number of training examples remains fixed. Existing streaming feature selection methods focus on removing irrelevant and redundant features and selecting the most relevant features, but they ignore the interaction between features. A feature might have little correlation with the target concept by itself, but, when it is combined with some other features, they can be strongly correlated with the target concept. In other words, the interactive features contribute to the target concept as an integer greater than the sum of individuals. Nevertheless, most of the existing streaming feature selection methods treat features individually, but it is necessary to consider the interaction between features. In this article, we focus on the problem of feature interaction in feature streams and propose a new streaming feature selection method that can select features to interact with each other, named Streaming Feature Selection considering Feature Interaction (SFS-FI). With the formal definition of feature interaction, we design a new metric named interaction gain that can measure the interaction degree between the new arriving feature and the selected feature subset. Besides, we analyzed and demonstrated the relationship between feature relevance and feature interaction. Extensive experiments conducted on 14 real-world microarray data sets indicate the efficiency of our new method.

Feature interaction in software product line engineering: A systematic mapping study

Context: Software product lines (SPL) engineering defines a set of systems that share common features and artifacts to achieve high productivity, quality, market agility, low time to market, and cost. An SPL product is derived from a configuration of features which need to be compounded together without violating their particular specifications. While it is easy to identify the behavior of a feature in isolation, specifying and resolving interactions among features may not be a straightforward task. The feature interaction problem has been a challenging subject for decades.Objective: This study aims at surveying existing research on feature interaction in SPL engineering in order to identify common practices and research trends.Method: A systematic mapping study was conducted with a set of seven research questions, in which the 35 studies found are mainly classified regarding the feature interaction solution presented: detection, resolution and general analysis.Results: 43% of the papers deal with feature interaction at early phases of a software lifecycle. The remaining is shared among the other categories: source code detection, resolution and analysis. For each category, it was also identified the main strategies used to deal with interactions.Conclusions: The findings can help to understand the needs in feature interaction for SPL engineering, and highlight aspects that still demand an additional investigation. For example, often strategies are partial and only address specific points of a feature interaction investigation.

Combinatorial Interaction Testing of Software Product Lines: A Mapping Study

Software Product Line (SPL) is a software engineering paradigm that is inspired by the concept of reusability of common features, formulated for different software product. Complete testing on entire SPL is known to be unfeasible, due to the very large number of possible products to be produced, configured using a subset or all possible features in the SPL. This paper reports a systematic mapping study (SMS) of relevant primary studies as the evidence on the application of Combinatorial Interaction Testing (CIT) for SPL. In CIT, one has to construct a covering array, which is a set of configurations having valid feature combinations and every combination of t features appears at least once in the array. This is also known as t-wise testing. By following the systematic mapping study guidelines, we have selected and filtered 44 primary studies for review. The most prominent CIT techniques in aiding SPL testing are those based on greedy algorithms followed by meta-heuristics algorithms. The motivation of SPL testing is to anticipate the feature interaction problem, in which the majority of the works were reported to leverage test configuration selection approach, while some employed test configuration prioritization approach. Numerous works have been reported, but only few works managed to demonstrate their scalability, as most primary studies only deal with low strength (t is less than 4) of t-wise testing.

Policy conflicts in home automation

The nature of home automation is introduced. It is argued that end users should be able to define how the home system reacts to changing circumstances. Policies are employed as user-defined rules for how this should happen. The architecture of the Homer home automation system is briefly overviewed. The Homer policy system and the Homeric policy language it supports are explained. A technique is described for offline conflict analysis among policies (the analogue of the feature interaction problem). A substantial worked example shows how conflict detection is performed on a range of sample home policies.

Feature interaction in a federated communications-enabled collaboration platform

Existing online collaboration tools and platforms provide basic communications integration and the ability to include some real-time information sources. Moreover, users prefer to be able to choose which collaboration tool they use for a given interaction, and over the course of long-term collaboration, will typically use a variety of tools, including email, instant messages, wikis, blogs, web conferences, and shared documents.For enterprise use there is a need to integrate the various tools as well as link with existing intelligent communication systems to support long-term collaborations in a variety of ways. Due to the number and different nature of collaboration services used in enterprises today, building such a federated collaboration platform is challenging. Collaboration tools differ in terms of storage model, APIs, content organization, addressing, formats, user authentication, and user interface. By the very nature of such systems they include a large number of independently developed features and services and thus provide a strong potential for feature interactions. This paper presents novel work on feature interaction analysis in collaboration environments and presents an approach to detect and resolve such interactions where the collaboration space is used as a communication endpoint.In this paper ConnectedSpaces is used as a basis to carry out a detailed analysis of feature interaction problems in collaboration environments. ConnectedSpaces is a new model for federated collaboration environments. Like a number of existing systems, ConnectedSpaces uses a collaboration space as the basic construct. ConnectedSpaces enables the user to work directly in the client application of their choice; this is illustrated with plug-ins for MS Outlook, Internet Explorer and Skype.This paper presents distinctive characteristics of ConnectedSpaces, including views, spaces as communication endpoints, space persistence and structuring, and embedded objects. Using these features, new types of feature interactions for collaboration platforms are categorized and analyzed. The work presented in this paper focuses on handling feature interactions which are caused by using ConnectedSpaces as a communication endpoint with enterprise communication platforms. This work is novel as it is the first investigation into feature interactions with collaboration platforms. Our approach uses a runtime feature interaction technique which can cope with features being provided by different organizations.

Extracting Feature Sequences in Software Vulnerabilities Based on Closed Sequential Pattern Mining

Feature Extraction is significant for determining security vulnerabilities in software. Mining closed sequential patterns provides complete and condensed information for non-redundant frequent sequences generation. In this paper, we discuss the feature interaction problem and propose an efficient algorithm to extract features in vulnerability sequences. Each closed sequential pattern represents a feature in software vulnerabilities. We explore how to efficiently maintain closed sequential patterns in vulnerability sequences. A compact structure WClosedTree is designed to keep closed sequential patterns, and its nice properties are carefully studied. Two main pruning strategies, backwards super pattern condition and equivalent position information condition, are developed to remove frequent but non-closed sequential patterns in WClosedTree . During the process of maintaining WClosedTree , the weight metric of each feature sequence is calculated to better meet the needs of decision makers. Thus, the proposed algorithm can efficiently extract features from vulnerability sequences. The experimental results show that the proposed algorithm significantly improves the runtime efficiency for mining closed sequential patterns, and the feature interaction framework implements feature extraction in software vulnerabilities.

Detecting OAM&P design defects using a feature interaction approach

In the telecommunications service engineering world, the feature interaction (FI) problem is defined as the situation in which running services at the same time undesirably alters their behaviour. It is also known as the service interaction problem. FIs may occur between various types of services, ranging from signalling protocol features to value-added end-user services. The problem has been widely investigated for those types of services. However, sufficient attention has not been paid to operation, administration, management and provisioning (OAM&P) features like creating a new customer account or registering a new gateway as part of the network. The present paper addresses the detection of OAM&P FIs in Internet telephony architectures. The solution consists in an adapted version of a known FI detection method. A simple feature modelling language and an FI detection procedure are proposed. A validation with a case study is also reported. Copyright © 2011 John Wiley & Sons, Ltd.

Online approach to feature interaction problems in middleware based system

As a popular infrastructure for distributed systems running on the Internet, middle-ware has to support much more diverse and complex interactions for coping with the drastically increasing demand on information technology and the extremely open and dynamic nature of the Internet. These supporting mechanisms facilitate the development, deployment, and integration of distributed systems, as well as increase the occasions for distributed systems to interact in an undesired way. The undesired interactions may cause serious problems, such as quality violation, function loss, and even system crash. In this paper, the problem is studied from the perspective of the feature interaction problem (FIP) in telecom, and an online approach to the detection and solution on runtime systems is proposed. Based on a classification of middleware enabled interactions, the existence of FIP in middleware based systems is illustrated by four real cases and a conceptual comparison between middleware based systems and telecom systems. After that, runtime software architecture is employed to facilitate the online detection and solution of FIP. The approach is demonstrated on J2EE (Java 2 Platform Enterprise Edition) and applied to detect and resolve all of the four real cases.

Feature interaction: the security threat from within software systems

Security engineering is about protecting assets from harm. The feature interaction problem occurs when the composition of features leads to undesirable system behaviours. Usually, this problem manifests itself as conflicting actions of features on a shared context. Security requirements may be violated by feature interactions creating security vulnerabilities which can potentially be exploited by attackers. In thi sp aper, we discuss the feature interaction problem and some of its possible implications for security requirements. The paper concludes that (1) the detection of the violation of security requirements by feature interactions is not different from other types of requirements - what differs is the impact of such violation; and (2)feature interaction detection approaches can be used as a means for vulnerability analysis.

Detecting feature interactions in Web services with model checking techniques

As a platform-independent software system, a Web service is designed to offer interoperability among diverse and heterogeneous applications. With the introduction of service composition in the Web service creation, various message interactions among the atomic services result in a problem resembling the feature interaction problem in the telecommunication area. This article defines the problem as feature interaction in Web services and proposes a model checking-based detection method. In the method, the Web service description is translated to the Promela language — the input language of the model checker simple promela interpreter (SPIN), and the specific properties, expressed as linear temporal logic (LTL) formulas, are formulated according to our classification of feature interaction. Then, SPIN is used to check these specific properties to detect the feature interaction in Web services.

Using semi-formal methods for detecting interactions among smart homes policies

Feature Interaction is a problem mostly considered in the telecommunications domain. Many solutions for detecting interactions between telephony features have been reported. In this paper, we investigate the feature interaction problem beyond the traditional telecommunications domain and look at interactions between policies in other domains. We propose the use of semi-formal methods for detecting interactions between policies in the smart homes domain. The novelty of this research is threefold: firstly, a six step semi-formal approach, called IRIS (Identifying Requirements Interactions using Semi-formal methods), for detecting interactions is presented. A major component within IRIS, which is an interaction taxonomy, is also presented. Secondly, we extend the scope of the problem of feature interactions beyond telecommunication features and investigate interactions between policies in the smart homes domain. Thirdly, in order to show how IRIS is used to detect interactions between policies, a case study of the smart homes domain is conducted. A complete description of the results obtained is also provided. Our approach was successfully applied to the smart homes domain and was able to discover 83 interactions among 35 user policies using only 525 pairwise comparisons as opposed to 630 a human expert would have to do. These results support the paper’s main claim of being able to use the semi-formal approach IRIS to detect interactions between policies. Furthermore, these results are to date the most complete publication of interactions between policies in the smart homes domain.

A Model-Based Approach to Detecting Feature Interactions

To adapt to continually changing businesses, many software systems have to undergo evolutions through plugging new extensions into common base subsystems. Although it can facilitate concurrent development and deployment, this evolution strategy faces the problem of unexpected feature interactions between extensions. So far, formal method is still one of the most effective methods to detect feature interaction problems. The method has been proved to be successful by some small scale experiments. However, it also faces some challenges, e.g., the non-monotonicity of extension, the fast increase of extension combinations and the lack of extension details due to market competitions. Actually, many feature interactions are caused by the inappropriate modifications to the base subsystem and the existing extensions by new extensions. Based on this observation, the paper proposes a new approach for analyzing the causes of feature interactions and for formulating the corresponding constraints such that the conflicts with the same reason can be avoided. The approach has been applied to the analysis of the feature interactions of telecom systems. The experimental results show that most of the conflict-prone behaviors can be discovered quickly. In addition, the approach can help preserve the stability of the original base model and extension models, bypass the problem of extension combinations, and eliminate the requirement to publish the details of all extension models.

Managing feature interactions between distributed SIP call control services

The Session Initiation Protocol (SIP) is widely used as a call control protocol for Voice over IP (VoIP), and indeed commercial implementations are readily available off-the-shelf. SIP supports flexible service provisioning not only through third parties, but also end-users. Laboratory experience shows that as these services are interworking they are subject to the feature interaction problem. Feature interactions may considerably delay service deployment and hence are a threat to rapid service provisioning. This paper investigates the feature interaction problem in SIP-based services and investigates the application of a pragmatic approach. This runtime approach does not require any detailed information about the services and hence can be applied in a competitive market. Furthermore, the technique is particularly strong in handling interactions between distributed services - a key characteristic of SIP-based services. Moreover, the approach is fully distributed without any centralised components, and includes detection and resolution of feature interactions.

Generic semantics of feature diagrams

Feature Diagrams (FDs) are a family of popular modelling languages used to address the feature interaction problem, particularly in software product lines, FDs were first introduced by Kang as part of the FODA (Feature-Oriented Domain Analysis) method back in 1990. Afterwards, various extensions of FODA FDs were introduced to compensate for a purported ambiguity and lack of precision and expressiveness. However, they never received a formal semantics, which is the hallmark of precision and unambiguity and a prerequisite for efficient and safe tool automation. The reported work is intended to contribute a more rigorous approach to the definition, understanding, evaluation, selection and implementation of FD languages. First, we provide a survey of FD variants. Then, we give them a formal semantics, thanks to a generic construction that we call Free Feature Diagrams (FFDs). This demonstrates that FDs can be precise and unambiguous. This also defines their expressiveness. Many variants are expressively complete, and thus the endless quest for extensions actually cannot be justified by expressiveness. A finer notion is thus needed to compare these expressively complete languages. Two solutions are well-established: succinctness and embeddability, that express the naturalness of a language. We show that the expressively complete FDs fall into two succinctness classes, of which we of course recommend the most succinct. Among the succinct expressively complete languages, we suggest a new, simple one that is not harmfully redundant: Varied FD (VFD). Finally, we study the execution time that tools will need to solve useful problems in these languages.

Towards a classification of web service feature interactions

The rapid introduction of new web services into a dynamic business environment can lead to undesirable interactions that negatively affect service quality and user satisfaction. In previous work, we have demonstrated how such interactions between web services can be modeled as feature interactions. In this paper, we outline a classification of web service feature interactions. The goals of this classification are to understand the scope of the feature interaction problem in the web services domain, and to propose a benchmark against which to assess the coverage of solutions to this problem. As there is no standard set of web services that one could use as examples, we illustrate the interactions using a fictitious e-commerce scenario.

Editorial for the SoSyM issue 2006/02

This issue consists of two parts: The first part has three regular papers. The second part contains a special section on Service-Based Software Engineering edited by Manfred Broy, Heinrich Hussmann, Ingolf H. Kruger and Bernhard Schatz. In the regular paper “Extending UML with Coordination Contracts” by Kevin Lano and Jose Fiadeiro a mechanism for separating software components from the glue (interactions) that tie the components together is introduced. The separation supports rapid evolution by allowing developers to adapt the glue (interactions) between the components without modifying the components themselves. Nicolas Gorse,Luigi Logrippo and Jacques Sincennes in their paper “FormalDetectionof Feature Interactions with Logic Programming and LOTOS” present a solution to the feature interaction problem that occurs in telecommunication, automobile, and other systems that consist of many interacting functional parts. The use of a two phased filter and test approach that first detects possible faulty interactions and then tests whether the faulty interactions can actually occur.