Influential factors of aligning Spotify squads in mission-critical and offshore projects – a longitudinal embedded case study

Changing the development process of an organization is one of the toughest and riskiest decisions. This is particularly true if the known experiences and practices of the new considered ways of working are relative and subject to contextual assumptions. Spotify engineering culture is deemed as a new agile software development method which increasingly attracts large-scale organizations. The method relies on several small cross-functional self-organized teams (i.e., squads). The squad autonomy is a key driver in Spotify method, where a squad decides what to do and how to do it. To enable effective squad autonomy, each squad shall be aligned with a mission, strategy, short-term goals and other squads. Since a little known about Spotify method, there is a need to answer the question of: How can organizations work out and maintain the alignment to enable loosely coupled and tightly aligned squads? In this paper, we identify factors to support the alignment that is actually performed in practice but have never been discussed before in terms of Spotify method. We also present Spotify Tailoring by highlighting the modified and newly introduced processes to the method. Our work is based on a longitudinal embedded case study which was conducted in a real-world large-scale offshore software intensive organization that maintains mission-critical systems. According to the confidentiality agreement by the organization in question, we are not allowed to reveal a detailed description of the features of the explored project

Stochastic Answer Networks for Machine Reading Comprehension

We propose a simple yet robust stochastic answer network (SAN) that simulates multi-step reasoning in machine reading comprehension. Compared to previous work such as ReasoNet which used reinforcement learning to determine the number of steps, the unique feature is the use of a kind of stochastic prediction dropout on the answer module (final layer) of the neural network during the training. We show that this simple trick improves robustness and achieves results competitive to the state-of-the-art on the Stanford Question Answering Dataset (SQuAD), the Adversarial SQuAD, and the Microsoft MAchine Reading COmprehension Dataset (MS MARCO).Comment: 11 pages, 5 figures, Accepted to ACL 201

The Schnitzel Squad

Postcard from Cruz Morey, during the Linfield College Semester Abroad Program at the Austro-American Institute of Education in Vienna, Austri

Reinforced Mnemonic Reader for Machine Reading Comprehension

In this paper, we introduce the Reinforced Mnemonic Reader for machine reading comprehension tasks, which enhances previous attentive readers in two aspects. First, a reattention mechanism is proposed to refine current attentions by directly accessing to past attentions that are temporally memorized in a multi-round alignment architecture, so as to avoid the problems of attention redundancy and attention deficiency. Second, a new optimization approach, called dynamic-critical reinforcement learning, is introduced to extend the standard supervised method. It always encourages to predict a more acceptable answer so as to address the convergence suppression problem occurred in traditional reinforcement learning algorithms. Extensive experiments on the Stanford Question Answering Dataset (SQuAD) show that our model achieves state-of-the-art results. Meanwhile, our model outperforms previous systems by over 6% in terms of both Exact Match and F1 metrics on two adversarial SQuAD datasets.Comment: Published in 27th International Joint Conference on Artificial Intelligence (IJCAI), 201