Abstract In the context of machine learning, classification is the procedure of predicting the class to which each element of a population belongs to. Most classification functions, for real world problems, are imperfect and thus require rigorous analysis for use in safety-critical applications such as health care. This paper proposes a software architecture for improving the trustworthiness and explainability of AI-based classifiers. The architecture combines a search-based approach with machine-learned explanations and satisfiability solving, to provide an indication of classification confidence and counterfactual explanation rules that are deductively verified to be consistent with the classifier. An implementation of the proposed architecture is evaluated on a medical case study of prognosis of Acute Coronary Syndrome (ACS). The evaluation shows that the proposed architecture is consistently able to complement each individual classification with an indication of confidence and an explanation, which is formally verified for consistency with the classifier. This contributes to foster trustworthy and explainable classification.

Until recently, the state-of-the-art of Software Product Line (SPL) configuration and verification automation consisted of a collection of ad-hoc approaches tightly coupling a single input Variability Modeling Language (VML) with a single constraint solver. To remedy this situation, a novel generic model-driven architecture was then proposed that enables using a variety of VMLs and solvers. The key ideas of this proposal were (a) the use of a standard logical language (CLIF) as a pivot between VMLs and solvers, and (b) the use of a standard data exchange format (JSON) to explicilty and declaratively specify the abstract syntax and semantics of the VMLs to be used in an SPL engineering project and the automated reasoning task to be performed by the solvers.

Verifying and configuring large Software Product Lines (SPL) requires automation tools. Current state-of-the-art approaches involve translating variability models into a formalism accepted as input by a constraint solver. There are currently no standards for variability modeling languages (VML). There is also a variety of constraint solver input languages. This has resulted in a multiplication of ad-hoc architectures and tools specialized for a single pair of VML and solver, fragmenting the SPL community. To overcome this limitation, we propose a novel architecture based on model-driven code generation, where the syntax and semantics of VMLs can be declaratively specified as data, and a standard, human-readable, formal pivot language is used between the VML and the solver input language. This architecture is the first to be fully generic by being agnostic to both VML and the solver paradigm. To validate the genericity of the approach, we have implemented a prototype tool together with declarative specifications for the syntax and semantics of two different VMLs and two different solver families. One VML is for classic, static SPL, and the other for run-time reconfigurable dynamic SPL with soft constraints to be optimized during configuration. The two solver families are Constraint Satisfaction Programs (CSP) and Constraint Logic Programs (CLP).

With the popularity of software defined networking architectures, the growing complexity of its use cases dictates the need for better auditability especially for security. In this paper, we aim at facilitating high-level management-plane policy configuration conformance auditing and their reflection in the data plane, to detect missing or spurious flow rules with respect to security policies. To this end, we propose an efficient conformance checking approach based on an intentional northbound interface as well as traces of management, control and data plane. Leveraging a proof-of-concept implementation of our approach, we compare its conformance-checking runtime and precision against a direct method on virtual topologies and find that it significantly improves scalability. We conclude by proposing directions for further enhancements extending the techniques presented herein.

Abstract Handling large-scale software variability is still a challenge for many organizations. After decades of research on variability management concepts, many industrial organizations have introduced techniques known from research, but still lament that pure textbook approaches are not applicable or efficient. For instance, software product line engineering—an approach to systematically develop portfolios of products—is difficult to adopt given the high upfront investments; and even when adopted, organizations are challenged by evolving their complex product lines. Consequently, the research community now mainly focuses on re-engineering and evolution techniques for product lines; yet, understanding the current state of adoption and the industrial challenges for organizations is necessary to conceive effective techniques. In this multiple-case study, we analyze the current adoption of variability management techniques in twelve medium- to large-scale industrial cases in domains such as automotive, aerospace or railway systems. We identify the current state of variability management, emphasizing the techniques and concepts they adopted. We elicit the needs and challenges expressed for these cases, triangulated with results from a literature review. We believe our results help to understand the current state of adoption and shed light on gaps to address in industrial practice.

We present a new approach to paratactic content aggregation in the context of generating hypertext summaries of OLAP and data mining discoveries. Two key properties make this approach innovative and interesting: (1) it encapsulates aggregation inside the sentence planning component, and (2) it relies on a domain independent algorithm working on a data structure that abstracts from lexical and syntactic knowledge.

This paper presents a quantitative evaluation of the portability to the stock market domain of the revision rule hierarchy used by the system STREAK to incrementally generate newswire sports summaries. The evaluation consists of searching a test corpus of stock market reports for sentence pairs whose (semantic and syntactic) structures respectively match the triggering condition and application result of each revision rule. The results show that at least 59% of all rule classes are fully portable, with at least another 7% partially portable.

We present a system for summarizing quantitative data in natural language, focusing on the use of a corpus of basketball game summaries, drawn from on-line news services, to empirically shape the system design and to evaluate our approach. Our initial corpus analysis revealed characteristics of textual summaries that challenge the capabilities of current language generation systems. In order to meet these challenges, we developed a revision-based model for summary generation and implemented it in our prototype system streak. A second, detailed corpus analysis was used to identify and encode the revision rules of the system. Finally, we carried out a quantitative evaluation, using several test corpora, to measure the robustness of the new revision-based model. Our results show that our new model improves both coverage and extensibility of the traditional language generation model.

Summaries typically convey maximal information in minimal space. In this paper, we describe an approach to summary generation that opportunistically folds information from multiple facts into a single sentence using concise linguistic constructions. Unlike previous work in generation, how information gets added into a summary depends in part on constraints from how the text is worded so far. This approach allows the construction of concise summaries, containing complex sentences that pack in information. The resulting summary sentences are, in fact, longer than sentences generated by previous systems. We describe two applications we have developed using this approach, one of which produces summaries of basketball games (STREAK) while the other (PLANDOC) produces summaries of telephone network planning activity; both systems summarize input data as opposed to full text. The applications implement opportunistic summary generation using complementary approaches. STREAK uses revision, creating a draft of essential facts and then using revision rules constrained by the draft wording to add in additional facts as the text allows. PLANDOC uses discourse planning, looking ahead in its text plan to group together facts which can be expressed concisely using conjunction and deleting repetitions. In this paper, we describe the problems for summary generation, the two domains, the linguistic constructions that the systems use to convey information concisely and the textual constraints that determine what information gets included.

In this paper, we discuss the different strategies used in COMET (COordinated Multimedia Explanation Testbed) for selecting words with which the user is familiar. When pictures cannot be used to disambiguate a word or phrase, COMET has four strategies for avoiding unknown words. We give examples for each of these strategies and show how they are implemented in COMET.