A Survey and Comparison of Industrial and Academic Research on the Evolution of Software Product Lines

Past research on software product lines has focused on the initial development of reusable assets and related challenges, such as cost estimation and implementation issues. Naturally, as software product lines are increasingly adopted throughout industry, their ongoing maintenance and evolution are getting more attention as well. However, it is not clear to what degree research is following this trend, and where the interests and demands of the industry lie. In this technical report, we provide a survey and comparison of selected publications on software product line maintenance and evolution at SPLC. In particular, we analyze and discuss similarities and differences of these papers with regard to their affiliation with industry and academia. From this, we infer directions for future research that pave the way for systematic and organized evolution of software product lines, from which industry may benefit as well.Comment: 8 page

Updates in a Rule based Language for Objects

The integration of object-oriented concepts into deductive databases has been investigated for a certain time now. Various approaches to incorporate updates into deduction have been proposed. The current paper presents an approach which is based on object versioning; different versions of one object may be created and referenced during an update-process. By means of such versions it becomes possible to exert explicit control on the update process during bottom-up evaluation in a rather intuitive way. The units for updates are the result sets of base methods, i.e. methods, whose results are stored in the object-base and are not defined by rules. However, the update itself may be defined by rules. Update-programs have fixpoint semantics; the fixpoint can be computed by a bottom-up evaluation according to a certain stratification

Cloud-Scale Entity Resolution: Current State and Open Challenges

Entity resolution (ER) is a process to identify records in information systems, which refer to the same real-world entity. Because in the two recent decades the data volume has grown so large, parallel techniques are called upon to satisfy the ER requirements of high performance and scalability. The development of parallel ER has reached a relatively prosperous stage, and has found its way into several applications. In this work, we first comprehensively survey the state of the art of parallel ER approaches. From the comprehensive overview, we then extract the classification criteria of parallel ER, classify and compare these approaches based on these criteria. Finally, we identify open research questions and challenges and discuss potential solutions and further research potentials in this field

Exploiting subspace distance equalities in Highdimensional data for knn queries

Efficient k-nearest neighbor computation for high-dimensional data is an important, yet challenging task. The response times of stateof-the-art indexing approaches highly depend on factors like distribution of the data. For clustered data, such approaches are several factors faster than a sequential scan. However, if various dimensions contain uniform or Gaussian data they tend to be clearly outperformed by a simple sequential scan. Hence, we require for an approach generally delivering good response times, independent of the data distribution. As solution, we propose to exploit a novel concept to efficiently compute nearest neighbors. We name it sub-space distance equality, which aims at reducing the number of distance computations independent of the data distribution. We integrate knn computing algorithms into the Elf index structure allowing to study the sub-space distance equality concept in isolation and in combination with a main-memory optimized storage layout. In a large comparative study with twelve data sets, our results indicate that indexes based on sub-space distance equalities compute the least amount of distances. For clustered data, our Elf knn algorithm delivers at least a performance increase of factor two up to an increase of two magnitudes without losing the performance gain compared to sequential scans for uniform or Gaussian data

An Empirical Analysis of Newcomers’ Contributions to Software-Engineering Conferences

Newcomer researchers play a key role in advancing research: They introduce new ideas and perspectives, have a high motivation, and can positively impact the performance of long-lasting teams. However, newcomers face obstacles when engaging in research—some of which they can overcome based on learning and mentoring (e.g., using research methods, scientific writing), but also potential biases of other researchers or unfair barriers (e.g., gate keeping, perceived expertise). In this paper, we report a study on newcomers’ contributions to three major software-engineering conferences, and what these contributions may indicate regarding potential obstacles. Precisely, we investigated to what extent newcomers contributed to the main tracks of the highly reputable software-engineering conferences ASE, ESEC/FSE, and ICSE, analyzing a total of 4,620 papers and 7,337 authors. Furthermore, we investigated whether the reviewing model impacted the extent of newcomers’ contributions, since all three conferences recently switched from single-blind to double-blind reviewing. The results indicate a decline in newcomer researchers contributing to the conferences, a trend that somewhat stabilized in recent years at a fortunately high level (i.e., more than 50% of authors for all conferences). Furthermore, for ICSE, we found an indicator that the changed reviewing model mitigated the declining trend, but this was not visible for the other conferences, and that more newcomers are involved in high-reputation papers

Towards multi-purpose main-memory storage structures: Exploiting sub-space distance equalities in totally ordered data sets for exact knn queries

Efficient knn computation for high-dimensional data is an important, yet challenging task. Today, most information systems use a column-store back-end for relational data. For such systems, multi-dimensional indexes accelerating selections are known. However, they cannot be used to accelerate knn queries. Consequently, one relies on sequential scans, specialized knn indexes, or trades result quality for speed. To avoid storing one specialized index per query type, we envision multipurpose indexes allowing to efficiently compute multiple query types. In this paper, we focus on additionally supporting knn queries as first step towards this goal. To this end, we study how to exploit total orders for accelerating knn queries based on the sub-space distance equalities observation. It means that non-equal points in the full space, which are projected to the same point in a sub space, have the same distance to every other point in this sub space. In case one can easily find these equalities and tune storage structures towards them, this offers two effects one can exploit to accelerate knn queries. The first effect allows pruning of point groups based on a cascade of lower bounds. The second allows to re-use previously computed sub-space distances between point groups. This results in a worst-case execution bound, which is independent of the distance function. We present knn algorithms exploiting both effects and show how to tune a storage structure already known to work well for multi-dimensional selections. Our investigations reveal that the effects are robust to increasing, e.g., the dimensionality, suggesting generally good knn performance. Comparing our knn algorithms to well-known competitors reveals large performance improvements up to one order of magnitude. Furthermore, the algorithms deliver at least comparable performance as the next fastest competitor suggesting that the algorithms are only marginally affected by the curse of dimensionality