Software Engineering

Software developers must interact with large amounts of different types of information and perform many different activities to build a software system. To ease the finding of information and hone workflows, there has been growing interest in building recommenders that are intended to help software developers work more effectively. Building an effective recommender requires a deep understanding of the problem that is the target of a recommender, analysis of different aspects of the approach taken to perform the recommendations and design and evaluation of the mechanisms used to present recommendations to a developer. In this chapter, we outline the different steps that must be taken to develop an effective recommender system to aid software development.

System behaviour is proposed as the core object of software development. The system comprises both the software machine and the problem world. The behaviour of the problem world is ensured by the combination of its given properties and the interacting behaviour of the machine. The fundamental requirements do not mandate specific system behaviour but demand that the behaviour exhibit certain desirable properties and achieve certain effects. These fundamental requirements therefore include usability, safety, reliability and others commonly regarded as ‘non-functional’. A view of behaviour content and structure is presented, based on the Problem Frames approach, leading to a specification in terms of concurrent behaviour instances created and controlled within a tree structure. Development method is not addressed in this short paper; nor is software architecture. For brevity, and clearer visibility of the thread of the paper’s theme, much incidental, explanatory, illustrative and detailed material is relegated to end notes. A final section summarises the claimed value of the approach in addressing the characteristic challenges of cyber-physical systems.

Data replication is a common technique for programming distributed systems, and is often important to achieve performance or reliability goals. Unfortunately, the replication of data can compromise its consistency, and thereby break programs that are unaware. In particular, in weakly consistent systems, programmers must assume some responsibility to properly deal with queries that return stale data, and to avoid state corruption under conflicting updates. The fundamental tension between performance (favoring weak consistency) and correctness (favoring strong consistency) is a recurring theme when designing concurrent and distributed systems, and is both practically relevant and of theoretical interest.

In this course, we investigate how to understand and formalize consistency guarantees, and how we can determine if a system implementation is correct with respect to such specifications. We start by examining consensus, a classic problem in distributed systems, and then proceed to study various specifications and implementations of eventually consistent systems.

The mining of software archives has enabled new ways for increasing the productivity in software development: Analyzing software quality, mining project evolution, investigating change patterns and evolution trends, mining models for development processes, developing methods of integrating mined data from various historical sources, or analyzing natural language artifacts in software repositories, are examples of research topics. Software repositories include various data, ranging from source control systems, issue tracking systems, artifact repositories such as requirements, design and architectural documentation, to archived communication between project members. Practitioners and researchers have recognized the potential of mining these sources to support the maintenance of software, to improve their design or architecture, and to empirically validate development techniques or processes. We revisited software mining studies that were published in recent years in the top venues of software engineering, such as ICSE, ESEC/FSE, and MSR. In analyzing these software mining studies, we highlight different viewpoints: pursued goals, state-of-the-art approaches, mined artifacts, and study replicability. To analyze the mining artifacts, we (lexically) analyzed research papers of more than a decade. In terms of replicability we looked at existing work in the field in mining approaches, tools, and platforms. We address issues of replicability and reproducibility to shed light onto challenges for large-scale mining studies that would enable a stronger conclusion stability.