Abstract: Complex software-intensive systems are increasingly relied upon for all kinds of activities in society, leading to the requirement that these systems should be resilient to changes that may occur to the system, its environment, or its goals. Traditionally, resilience has been achieved either through: (i) low-level mechanisms embedded in the implementation (e.g., exception handling, timeouts, redundancies), which are unable to detect subtle but important anomalies (e.g., progressive performance degradation); or (ii) human oversight, which is costly and unreliable. Architecture-based self-adaptation (ABSA) is regarded as a promising approach to improve the resilience and reduce the development/operation costs of such systems. Although researchers have illustrated the benefits of ABSA through a number of small-scale case studies, it remains to be seen whether ABSA is truly effective in handling changes at run-time in industrial-scale systems. In this paper, we report on our experience applying an ABSA framework (Rainbow) to a large-scale commercial software system, called Data Acquisition and Control Service (DCAS), which is used to monitor and manage highly populated networks of devices in renewable energy production plants. In the approach followed, we have replaced some of the existing adaptive mechanisms embedded in DCAS by those advocated by ABSA proponents. This has allowed us to assess the development costs associated with the reengineering of adaptive mechanisms when using an ABSA solution, and to make effective comparisons, in terms of operational performance, between a baseline industrial system and one that uses ABSA. Our results show that using the ABSA concepts as embodied in Rainbow enabled an independent team of developers to: (i) effectively implement the adaptation behavior required from such industrial systems; and (ii) obtain important benefits in terms of maintainability and extensibility of adaptation mechanisms.

Abstract: As the size of software systems increases, the algorithms and data structures of the computation no longer constitute the major design problems. When systems are constructed from many components, the organization of the overall system -- the software architecture -- presents a new set of design problems. This level of design has been addressed in a number of ways including informal diagrams and descriptive terms, module interconnection languages, templates and frameworks for systems that serve the needs of specific domains, and formal models of component integration mechanisms.

In this paper we provide an introduction to the emerging field of software architecture. We begin by considering a number of common architectural styles upon which many systems are currently based and show how different styles can be combined in a single design. Then we present six case studies to illustrate how architectural representations can improve our understanding of complex software systems. Finally, we survey some of the outstanding problems in the field, and consider a few of the promising research directions.

Abstract: Temporal logic specifications play an important role in a wide range of software analysis tasks, such as model checking, automated synthesis, program comprehension, and runtime monitoring. Given a set of positive and negative examples, specified as traces, LTL learning is the problem of synthesizing a specification, in linear temporal logic (LTL), that evaluates to true over the positive traces and false over the negative ones. In this paper, we propose a new type of LTL learning problem called constrained LTL learning, where the user, in addition to positive and negative examples, is given an option to specify one or more constraints over the properties of the LTL formula to be learned. We demonstrate that the ability to specify these additional constraints significantly increases the range of applications for LTL learning, and also allows efficient generation of LTL formulas that satisfy certain desirable properties (such as minimality). We propose an approach for solving the constrained LTL learning problem through an encoding in first-order relational logic and reduction to an instance of the maximal satisfiability (MaxSAT) problem. An experimental evaluation demonstrates that ATLAS, an implementation of our proposed approach, is able to solve new types of learning problems while performing better than or competitively with the state-of-the-art tools in LTL learning.

Abstract: In this paper, we argue that the reality of today's software systems requires us to consider uncertainty as a first-class concern in the design, implementation, and deployment of those systems. We further argue that this induces a paradigm shift, and a number of research challenges that must be addressed.

Abstract: One common approach to self-adaptive systems is to incorporate a control layer that monitors a system, supervisorily detects problems, and applies adaptation strategies to fix problems or improve system behavior. While such approaches have been found to be quite effective, they are typically limited to carrying out a single adaptation at a time, delaying other adaptations until the current one finishes. This in turn leads to a problem in which a time-critical adaptation may have to wait for an existing long-running adaptation to complete, thereby missing a window of opportunity for that adaptation. In this paper we improve on existing practice through an approach in which adaptations can be preempted to allow for other time-critical adaptations to be scheduled. Scheduling is based on an algorithm that maximizes time-related utility for a set of concurrently executing adaptations.

Abstract: This paper is an experience report on a first attempt to develop and apply a new form of software: a fullservice testbed designed to evaluate alternative software dependability technologies, and to accelerate their maturation and transition into project use. The SCRover testbed includes not only the specifications, code, and hardware of a public safety robot, but also the package of instrumentation, scenario drivers, seeded defects, experimentation guidelines, and comparative effort and defect data needed to facilitate technology evaluation experiments. The SCRover testbed’s initial operational capability has been recently applied to evaluate two architecture definition languages (ADLs) and toolsets, Mae and AcmeStudio. The testbed evaluation showed (1) that the ADL-based toolsets were complementary and costeffective to apply to mission-critical systems; (2) that the testbed was cost-effective to use by researchers; and (3) that collaboration in testbed use by researchers and the Jet Propulsion Laboratory (JPL) project users resulted in actions to accelerate technology maturity and transition into project use. The evaluation also identified a number of lessons learned for improving the SCRover testbed, and for development and application of future technology evaluation testbeds.

Abstract: Problem Space versus Solution Space Requirements engineering is concerned fundamen tally with the shape of the problem space. Its primary goal is to determine the dimensions of the problem that the software system is to solve. In contrast, software architecture is concerned with the shape of the solution space. Its primary goal is to determine the structure of a solution to a problem posed by a set of requirements. Thus, understanding the role of software architecture in requirements engineering is largely a matter of understanding the dynamic relationship between identification of a problem to be solved and a description of its solution.

Abstract: Abstract Software systems are designed and implemented with assumptions about the environment. However, once a system is deployed, the actual environment may deviate from its expected behavior, potentially leading to violations of desired properties. Ideally, a system should be robust to continue establishing its most critical requirements even in the presence of possible deviations in the environment. To enable systematic design of robust systems against environmental deviations, this work proposes a rigorous behavioral notion of robustness for software systems. Then, it presents a technique called behavioral robustification, which involves two tactics to systematically and rigorously improve the robustness of a system design against potential deviations.

Specifically, the robustness of a system is defined as the largest set of deviating environmental behaviors under which the system is capable of guaranteeing a desired property. Then, we present an approach to compute robustness based on this definition. On the other hand, the system is not robust against an environment when the environment exhibits deviations causing a violation of the desired property. The robustification method finds a redesign that is capable of satisfying the property under such a deviated environment. In particular, two tactics, namely robustification-by-control and robustification-by-specification-weakening, are introduced. The robustification-by-control tactic formulates the robustification problem as a multi-objective optimization problem with the goal of guaranteeing the desired property while maximally preserving the existing functionality and minimizing the cost of changes to the original design. Then, the specification-weakening tactic is used alongside the control tactic, which allows weakening the property to generate more feasible redesigns that retain more functionality or have a lower cost.

The proposed robustness computation and robustification method are implemented in a tool named Fortis. The applicability and efficiency of these approaches are evaluated through experimental results across five case studies, including a radiation therapy machine, an electronic voting machine, network protocols, a transportation fare system, and an infusion pump machine.

Abstract: To understand the context of current research, it is essential to understand how current results evolved from early fundamental papers. These classic papers develop timeless ideas that transcend technology changes, and the ideas embodied in their solutions often apply to current problems (and, indeed, are reinvented by researchers who are ignorant of the classic literature).

Criteria for selection of the papers in the Canon include

• Papers are selected for their overall significance. The papers should usually be the principal or definitive papers about an idea, rather than expository papers written after the idea has matured (these may appear in other reading lists). A rule of thumb is that the paper in the Canon should be the one you'd cite as the fundamental paper in its area.
• Classic, timeless papers prove their significance over time. It follows that the papers of the Canon will generally be published a minimum of 12-15 years ago.
• Some of the entries may be books. In these cases, the books should be important in their own right, but the entry should highlight one or a few specific passages in the book to focus on. Students should develop a sense of the rest of the book as well.
• Because the size of the Canon is limited, authors will rarely be represented by multiple papers, and redundancy will generally be avoided.
• All students should be familiar with all the papers, and the SE Research Core course should cover them. In practice this limits the size of the Canon to about 25 papers.

The Carnegie Mellon Software Engineering faculty has selected this set of papers as a canonical collection of significant classic papers that balances completeness with reasonable size. All our software engineering students should read and understand these papers (or selections from books). Notes in this document explain the significance of the papers. We will endeavor to cover them in the Core Course in Software Engineering Research, and other courses may feel free to assume that students have read them.

Abstract: The Computer Science for Practicing Engineers (CS4PE) course is a breath-oriented course covering key topics in computer science that represent critical foundational knowledge for anyone developing software. The course is designed for students and professionals who do not have a formal background in computer science, but currently write, or anticipate writing software as a key part of their job. CS4PE represents one important facet of Computation Thinking [Wing 2006]. Computational thinking is a fundamental skill for everyone, not just for computer scientists. To those core concepts essential to a particular professional discipline, we need to add computational thinking to every professional’s analytical ability. The purpose of this report is to describe the motivation for, and the development of, the CS4PE course. The aim is to describe why such a course is needed, the design of the course, our experience in delivering the course since its inception, and feedback received from a peer review of the course content and assignments during the 2010 Conference on Software Engineering Education and Training. We will also discuss how and why the course has evolved since its initial development and delivery, as well as future directions that we expect the course to move in.